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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bayesian Regression Using Pyro\n",
+    "\n",
+    "In this tutorial, we will explore how to do bayesian regression in Pyro, using a simple example adapted from Statistical Rethinking [[1](#References)]. In particular, we would like to explore the following:\n",
+    "\n",
+    "- Write a simple model using the sample Pyro primitive.\n",
+    "- Run inference using MCMC in Pyro, in particular, using the No U-Turn Sampler (NUTS) to get a posterior distribution over our regression parameters of interest. We also show an example of inference using Pyro's SVI.\n",
+    "- Learn about inference utilities such as `Predictive` and `log_likelihood`.\n",
+    "- Learn how we can use effect-handlers in Pyro to generate execution traces from the model, condition on sample statements, etc., and use this to implement various utilities that will be useful for MCMC. e.g. computing model log likelihood, generating empirical distribution over the posterior predictive, etc.\n",
+    "\n",
+    "## Tutorial Outline:\n",
+    "\n",
+    "1. [Dataset](#Dataset)\n",
+    "2. [Regression Model to Predict Divorce Rate](#Regression-Model-to-Predict-Divorce-Rate)\n",
+    "    - [Model-1: Predictor-Marriage Rate](#Model-1:-Predictor---Marriage-Rate)\n",
+    "       - [Posterior Distribution over the Regression Parameters](#Posterior-Distribution-over-the-Regression-Parameters)\n",
+    "       - [Prior Predictive Distribution](#Prior-Predictive-Distribution)\n",
+    "       - [Posterior Predictive Distribution](#Posterior-Predictive-Distribution)\n",
+    "       - [Predictive Utility With Effect Handlers](#Predictive-Utility-With-Effect-Handlers)\n",
+    "       - [Model Predictive Density](#Model-Predictive-Density)\n",
+    "       - [Regression with Pyro's Stochastic Variational Inference (SVI)](#SVI)\n",
+    "    - [Model-2: Predictor-Median Age of Marriage](#Model-2:-Predictor---Median-Age-of-Marriage)\n",
+    "    - [Model-3: Predictor-Marriage Rate and Median Age of Marriage](#Model-3:-Predictor---Marriage-Rate-and-Median-Age-of-Marriage)\n",
+    "    - [Divorce Rate Residuals by State](#Divorce-Rate-Residuals-by-State)\n",
+    "3. [Regression Model with Measurement Error](#Regression-Model-with-Measurement-Error)\n",
+    "    - [Effect of Incorporating Measurement Noise on Residuals](#Effect-of-Incorporating-Measurement-Noise-on-Residuals)\n",
+    "4. [References](#References)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from IPython.display import set_matplotlib_formats\n",
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "from torch import logsumexp\n",
+    "\n",
+    "import pyro\n",
+    "import pyro.distributions as dist\n",
+    "import pyro.poutine as poutine\n",
+    "import pyro.optim as optim\n",
+    "from pyro.infer import HMC, NUTS, MCMC\n",
+    "import pyro.ops.stats as stats\n",
+    "\n",
+    "plt.style.use(\"bmh\")\n",
+    "\n",
+    "assert pyro.__version__.startswith('1.8.0')\n",
+    "smoke_test = ('CI' in os.environ)\n",
+    "pyro.set_rng_seed(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <a id='Dataset'>Dataset</a>\n",
+    "\n",
+    "For this example, we will use the WaffleDivorce dataset from Chapter 05, Statistical Rethinking [[1](#References)]. The dataset contains divorce rates in each of the 50 states in the USA, along with predictors such as population, median age of marriage, whether it is a Southern state and, curiously, number of Waffle Houses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Loc</th>\n",
+       "      <th>Population</th>\n",
+       "      <th>MedianAgeMarriage</th>\n",
+       "      <th>Marriage</th>\n",
+       "      <th>Marriage SE</th>\n",
+       "      <th>Divorce</th>\n",
+       "      <th>Divorce SE</th>\n",
+       "      <th>WaffleHouses</th>\n",
+       "      <th>South</th>\n",
+       "      <th>Slaves1860</th>\n",
+       "      <th>Population1860</th>\n",
+       "      <th>PropSlaves1860</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>4.78</td>\n",
+       "      <td>25.3</td>\n",
+       "      <td>20.2</td>\n",
+       "      <td>1.27</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.79</td>\n",
+       "      <td>128</td>\n",
+       "      <td>1</td>\n",
+       "      <td>435080</td>\n",
+       "      <td>964201</td>\n",
+       "      <td>0.45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.93</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>2.05</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>6.33</td>\n",
+       "      <td>25.8</td>\n",
+       "      <td>20.3</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>0.74</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>AR</td>\n",
+       "      <td>2.92</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>26.4</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>1.22</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>111115</td>\n",
+       "      <td>435450</td>\n",
+       "      <td>0.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>37.25</td>\n",
+       "      <td>26.8</td>\n",
+       "      <td>19.1</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>379994</td>\n",
+       "      <td>0.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Location Loc  Population  MedianAgeMarriage  Marriage  Marriage SE  \\\n",
+       "0     Alabama  AL        4.78               25.3      20.2         1.27   \n",
+       "1      Alaska  AK        0.71               25.2      26.0         2.93   \n",
+       "2     Arizona  AZ        6.33               25.8      20.3         0.98   \n",
+       "3    Arkansas  AR        2.92               24.3      26.4         1.70   \n",
+       "4  California  CA       37.25               26.8      19.1         0.39   \n",
+       "\n",
+       "   Divorce  Divorce SE  WaffleHouses  South  Slaves1860  Population1860  \\\n",
+       "0     12.7        0.79           128      1      435080          964201   \n",
+       "1     12.5        2.05             0      0           0               0   \n",
+       "2     10.8        0.74            18      0           0               0   \n",
+       "3     13.5        1.22            41      1      111115          435450   \n",
+       "4      8.0        0.24             0      0           0          379994   \n",
+       "\n",
+       "   PropSlaves1860  \n",
+       "0            0.45  \n",
+       "1            0.00  \n",
+       "2            0.00  \n",
+       "3            0.26  \n",
+       "4            0.00  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DATASET_URL = \"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/WaffleDivorce.csv\"\n",
+    "dset = pd.read_csv(DATASET_URL, sep=\";\")\n",
+    "dset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us plot the pair-wise relationship amongst the main variables in the dataset, using `seaborn.pairplot`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x1080 with 42 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vars = [\n",
+    "    \"Population\",\n",
+    "    \"MedianAgeMarriage\",\n",
+    "    \"Marriage\",\n",
+    "    \"WaffleHouses\",\n",
+    "    \"South\",\n",
+    "    \"Divorce\",\n",
+    "]\n",
+    "sns.pairplot(dset, x_vars=vars, y_vars=vars, palette=\"husl\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the plots above, we can clearly observe that there is a relationship between divorce rates and marriage rates in a state (as might be expected), and also between divorce rates and median age of marriage.\n",
+    "\n",
+    "There is also a weak relationship between number of Waffle Houses and divorce rates, which is not obvious from the plot above, but will be clearer if we regress `Divorce` against `WaffleHouse` and plot the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gO796c7frqFA/fuLaIInE4iM7Vj8Yv2AZ++CQTCZHfsz7Hl4jGhIS0RAi7s9oSLjv4TUt/HpBZz+d3eBov9aaR6tFi/WyRXWEdZnbicb0Dq7GL1jGPjjkcqNfc32laBGP7M1NjkeEleLBVTqD8OsFnf10doODfk0v6+hawSJXbdBwgivBCVAtHd7VqQvGL1jGflXW2dnZkR9zMR1jq9IgEd0NEPWmYjF9cLp9EH69oLOfzm6w61dt2JQtm1oz2GCwn4nMVNAKHTF+wTL2LYdisTjyY77x3AkajqLWcFDK/dlwFG88d0ILv17Q2U9nN0cpNnJ5lot1NisN7QIDgFWpBK3QEeMXLGMfHIIouHLnmSxve8lpZiaiFOsOMxNR3vaS09x55uCMSl0KwhyFzn46urUmrC0XLIrVGrYTxGhCdzSb+l2/doxfsIx9t1JQufB3nskeGgz2M465+qNCJzfLdijV7T2Dy7rnwRs/f+ju55exbzmMUy58EOjsp4NbtWGzXrZYK1lU9mUd6Z4Hb/z8obufX8a25XDxcp77Hl5jslmi9IjFG8+d6Oqb/Ki5XtMxdSAot1ZN5lLd7phxFI3rnepo/Pyhu59fxrLl0D4JTYXDHSehBY0pWNM/o3Zrn8XcTSpqOBIdkVl/GD9/6O7nl7EMDu2T0BZijY6T0IImn9cvYLWjs9+o3CzbYauPWcy1sr7ZVGD8/KK7n1/GsltppWiRjrtx73LdneJ+1CS0oJmbmwtaoSM6+w3brdqwKdb7XwAvldW73oTx84fufn4Zy+DQPgntRLRB3o4cOQmtRWuMYqVosZiOjWyMIp/Pk0qlhn6cftHZbxhutqOoNGxKlu07DbVWKhJLTAzIbPAYP3/o7ueXsexWap+EFhen4yQ06G2hvEHTaDSGfgw/6Ow3SLeG7ZDzuo7yteZA5ifYdnMAZsPD+PlDdz+/jGXLwZ2E5o49PJS3mUlFO7YE2scoAHfZi4bDfQ+vDb31oFOu/mEM2m+QLbRBuFW9VkJ9CDOYdc+DN37+0N3PL2PZcgA3QLzz1c/mF/5Bhne++tkdb0C9LJQ3aHTI1e/EIP0G3ULr181Rbi3m1tIWwwgMoH8evPHzh+5+fhnLlkM73fRJ97JQ3qDpxi+o8RDozq9bBt1C69WtYbsV1iqWM7A6zJ3QvT/a+PlDdz+/jG1waN1QI1aZZizX8Yb6xnMnuPdzV6DhEI8I9abqOEYxSMLhcMftrW/b0ZDs+bb9tpcwkgBxnF8vtGeRtfDTQuvWLahVUUMhvRvmxs8fuvv5ZSzPrr374mkT9rHdF70slDdoCoVCx+29FA4Kwq8XFtMx6s2939j9tNA6uTlKUao3WQlwVdRapTTyY/aC8fOH7n5+6brlICJx4JeANwOzSqmsiHw/8Byl1L3DEuyH9hvqkhV3uzGO6b7odqG8QTM/33lQa9DftnvlOL9eGHQL7TC3puMGhfKIuo46MTmld70J4+cP3f380kvL4XeA24B/DDt/dY8Cbx20lF/aB5hvirk3UV0nwW1tbXXcPuhv271ynF8vDLqF1u5Wazpslt1U1JJlBx4YACqF7aAVOmL8/KG7n196GXN4PfAspVRZRBwApdRVEblpOGr90z7A3Ip+o7yh9oI6ZimGIMdDuvHrlUG20BzHoWzZxy6AFxSDvnaDxvj5Q3c/v/TScrDYF0xEZB7YHKjRAGifBPetWuzYSXBBcly3TZDjId34BYHtKPK1JmpiSotazEeRmpoJWqEjxs8fuvv5pZeWw33AH4jIzwCIyEngd4EPD8HLF+2T4G6SPESy2i7Zvbq6ys0339zxNUGNh0B3fqPCajoULZuaVzehsLXO9IJ2DdcdSrkN4+cD4xcsvQSHXwB+E/h7YAJ4AvjPwK8Nwcs3rRvq5uam1oXoJycng1boSNB+rdoJZevgAnjxpJ5rPrUwfv4wfsHSdXBQSlnAzwA/43Unbahx73QzBIbtKMqWOz+h2yWyDYbrnYbtsFZqsFysc61QZ7va5JVn53jBqfTIXXpJZf1R4CGl1MNKqXXvuecD55RS7zvmve8BXgOsKaVu8557J/Ba3LGMJ4F/opTa7ussOlAqlbRuORi/vRxWi/ko6tUyE5mpUWj1hfHzxzj7VRs21woWK8U61woWy8U6ywWL9bLF/jUfb5lO6h0cgH8D3L7vucvAx4COwQF4L3Av8Idtz30aeIdSqikivwm8A/i5Hny6YmFhYdC7HCjGz6ViuQvg9VI7YXJa31oTYPz8cr37KaUo1G2uFeosF+osFy3vscV2rfsVXZ/arvlV7YtegkMG2D8lNQ9MHfdGpdQDInLLvuc+1fbr54E39ODSNevr65w5c2YYux4IN7Kf366j8vYWUydODsFsMBg/f1wvfo5SbJQbO9/+r3mBYLlQp9LoPZNubiLKyUyMk+k433YixdkTwazh1Etw+ArwPwN/1Pbc64HHBuDx48BHBrCfA4jI8S8KkBvRr+m4q6L6XQDvRrx2g8T49UbDdlgtWW43UKHOU+tl1h/6BqtFi0aP9T/CAovp+E4QaP1cTMeIR3ZnGNyUiQd2HXoJDj8H/LmI/BDuGMGzgPPAq/wIiMgvAk3gA4dtX1tb48KFC0QiEWzb5u677+aee+5hZWWFVCpFOBymUCgwPz/P1tYWSinm5+dZXV1lcnKSUCjE0tISCwsLrK+vIyLMzMywvr5OJpPBtm3K5TKLi4usrKwQjUbJZrNsbGyQzWaxLItqtbqzPRaLkU6n2dzcZHp6mmq1Sq1W29meSCRIJpPkcjlmZ2cpFotYlrWzPZlMEovFyOfzzM3N0Ww2WVpa2tnezTmBOxYwinOq1Wqsrq72dE75fJ5Go3HgnLbzBRKZ6Z1zSk3NUMpt7GR91KtlJqfnKG9vISJMZKYobW+SmJjEcRysWoX0zDzFrXXC4QjReILc6lUSqTR2s0GjXtvZHonEiE1MUClsk5zM0rBqNK367vZYnGgsQbWUZyIzhVWp0GxaO9uj8QThSJRauUgqO02tVMS2mzvbY4kJQqEQtUqJyalZKoXtA+ckoRC51as9nVNiMk05nxvJOTm2TW71ak/n1M/n1O85Na06xdzG0D+n/edUsyFPkm+t5dhqRFguNVgpNtisOT1/lYmHhYVUmBMJ4cxcmimqnMomWMymsCqFtnMqkZ5MUtxcptF2TlPhBQqFwqF/T8fdI/wi3SYciUgIOA38MHAGd7zhA0qpy12+/xbg460Bae+5/wX4CeC8Uqpy2PsefPBBdfbs2a4cD2NpaUmbPP3DuBH8mo6iUGtSadgDsnLJrV7VOs/c+PljmH5KKbZrTZa9weBWa2C5WCdf6/3faSYe5lSmvSUQ52Q6xnQy4uubv5+Ww6VLl754/vz5O/o9dlctBxEJAyVgSin1G/0ebN8+7wLeDnzPUYFhEGQymWHteiCMs1/TURS9oDCMZNTEhN5zRIyfPwbh5yjFeqnBteLuoHDrZ7XH8QAB5lJRTmbinErHmI3Z3Dw/xclMnFRscEvb60JXwUEpZYvI14BZ4FqvBxGRDwEvB+ZE5Arwy7jZSXHg015k/LxS6id73fdx2PZgv60OmnH0G3ZQaOFoumxGC+Pnj178LNthpe3G38oKWi1ZNHscD4iEhIXJGCczMbc1kI5xMuOOB8TCu+MB5XyOVHZ8C/70MubwAeDjIvJ7wBV2V2ZFKfWZTm9USr35kKff3cOx+6ZcLjM3dzDlLMjqat346UIvfr3MURgEVq1CKjs9giP1h/Hzx2F+ZctmuVDnWisQeI83y42e/80lIyG3GygT51RrUDgTZz4VJdRFV47u188vvQSH1tLcv7LveQU8YyA2Q+CwIvRBV1c7zk8njvNTSlFtOD3PURgEuhd4N379oZQiV21ypZni757Y2tMdVKj33pLNJiKc2pcVdDITYyrhbzxA1+s3KHpZPuPpwxQZFisrKwcGVAddy3jQfjpxlJ9S7hyFomVj99hsHxRFzRfeM36dsR3Fetly5wZ4cwRagaDXyn0CzE9G9wSAVpfQxJDGA4Z5/R5eLvLJx7fYqjQ4mYkH0rPRUw1pEYkALwFuwu1aelAp1f1UvwCIRqMHngu6ulo7h/npxLVSk3s/8cRO99vdt83zvIVJLdY8Cof1LoFu/FzqTW88wFsvaLlgsVLsbzwgGhIW0nvHAk5lYixMxoiGR1v1eFjX7+HlIu//0ioRIdCejV7WVjoL/FcgiZvGegaoichrlVKDmAg3FLLZgxezvRhQi6CKAR3mpwsXL+f58FfLlJwIqZiwVrL4j5+7wltesMC5k6Nf62U/icngHTpxo/mV6s22sYDd1sBmpdHzvlLREAuTUU5lE5zyBoNPZeLMdTkeMAqG9fl+8vEtIgLxnbrxwfRs9BL6/i/gPwHvaq3GKiL/ynv+FUNwGwgbGxukUnuX1g26utpxfrpw38NrPHPC4pFKBFtBLCKohuKTj29pERzK+RyxhL7ZIuPop5Riq9rcMw7QWjiu2Md4wHQystMV1N4ayMTDbK9dY3rhVM/7HBXD+nzXyw1Ssb0BMIiejV6Cw+3A9+1bpvt3gV8cpNCgOeybeXsxoKCzlXRtOVhNh6v5OvHJyJ4skFhEWC/3/k1wGCRSwQeoTlzPfk1HsV7aOxZwrWixUqhTt3vrCgoJzKdi7qBwWwA4mY6RjB49HnA9Xz8/zKeibFcbxAPu2eglOFwDvgdoT1t9GX3MexgllnV4tA2yulo7R/kFhdV0KNSb1JoOc6koUWXt266YT+kxTmI39QhSR3E9+NWaDsuFOivFvQvGrZUseowBxMLCYjq2ryUQ58RktK/xgOvh+g2Du26d4f1fWoWGQyQWCqxno9dKcB8TkY8DS8DNwKuBtwxDbFBUq9WgFTqii1+rLnP7Ehd33TrD408WqFcdYhHBaiqayn1eBxr1YJYy7had/Ar1ZlsLwG0NXN2usF1f63lfqViYk61B4Z3MoBgzE4MdD9Dp+h3GsPzOnUzzFrh+spWUUh8TkRcCPwicAh4Bfkkp9bVhyQ2C630eQScGMZHPUYpS3aZYtw+skHruZBqxT/PNrxdYLzeYT0W569YZLcYbQP8881H7OUqxVWnsKR5zzWsVlKzexwNmkhG3+ycT250klo6TjodHslLojfz5njuZ5tzJ9PWxKquI3K6Uegj4t8PTGTzX6zyC4/A7ka8VFEqWjdMhJfV0tMLbX67n9Qs6T/84huXXdBSrxd0A0EoRXSlaWD32BYUFTkzGdrKB2scEEpHRpobu50b9fHWhl26lT4nIOvAh3NVYvzkkp4ESi3U/iBPEkhrd+u13K9SbfU3ksx1FySuw0ykotIhERp/e2y06u4F/v2rDPjAWcO2IUpLHEQvL7gQxb+G4jFPm6acXiYT0SA3dz7h/vrrTS3A4CdwFvBn4sog8CnwQ+IhSqveOyyHTupk2axUq5ECEiuUcedMPakmNdPr4LprD3K4W6ixMRoHdb3ed0t3qTW/do2Zv3QuxCX1TMXV2g+78WqUk9y8Yt1ysk6v2Pr90MhZuywpyxwIW03FmJiIHxgPq1bC2gQHG4/O9nullzMEGPgF8QkSSwOtw11t6F+7qqtrQfjM9l7L45LI7Ff/EZPTIm35QS2psbm4eW5zjMLdoSNiqNJmM736E+9PdlFJUGm5QaPS5AmelsL1TEEU3dHaDvX6HlZJsrSJa7qOU5GxbKcn2MYF0vPvve9fT9dMR3f380vP8bxFJAK8Bfgi4A/jrQUv5pf1m+tVCmFAIQNiu2ZzORg696beW1ChbNrlqk4atiIToayCvF6anj1/V8bDlPmaSEdbKDWqHTORrBYVCvel73aPkZPDpvkeho1urlORyweLyJqx98+pOqmg/pSQXDqSGui2B+ADGA3S8fu0Yv2DpZUD6VbhV4P4Rbj3pDwNvVUqtDMmtb9pvppmIjRBBgIY3WHdY98tiOsbVQo3tqo2I+4fZcBSOZXPxcn5orYdqtXpsQZ3DlvuIhEM8bSpOJhHdGYd4w7e76x6tlKyBLYbXsGokUnoWhQnSrdKwd7p/2lNE10u9Lx0dj4Q4ld43QSwTYz4VG2q3j86fLRi/oOml5fAu3MHoFyilnhySz0Bov5nOxNwETQVEw+4f2mGzDd947gS/9hffRKEIAQ7uSo+ZeHioXUu12vG50kct9/ETLz7NnWey2I67QmrJsslVBzsxp2nVB7q/QTJsN6XcuR/7xwKWCxbbtd7HAzLx8J4A0FpG2m8pyX7R+bMF4xc0vYw5PHeYIoOk/Wb6LZXAcWxAMTURodZwDp1teOeZLMlICMt2b7zRsDCVCJOKhYe6pkk38xyOWu7j+SfTbFYa1IZYXEfnXPNBuTlKsV5u7Fswrv9SkrOpKKfSMRYmI9w0NbETDCY1KyWp82cLxi9oOgYHEflFpdSve49/7ajXKaV+adBifmi/md4kFZzwBJWGw0bF/bZ3OnN4CtotM0mvxbHbn1trOENd06TbeQ6t5T4cpahYNmXLYb08/KU3dM7l7tWtYTt7agi3fq4U/ZWSbB8TWJiM7YwH5FavMr0w1dN+R4nOny0Yv6A5ruVwuu3xmWGKDJrWzXR1dZUlK8G9n7vC4mRsp1vmsIylIFZrTSQSXb2u1nSoWKMrwdkiEtMqEW0PR7mVLXvfWID7c6OPUpIJr5TkqT1zBNylo8PHjAfofO3A+PlFdz+/dAwOSqm3tv36TtyF9maALeBvlFKPDtFtICSTSe77u9Wu0lSDWK01mUweua01llBuBFdtLRrrLniNGqUUZSfCU6tlrwWwmyLaXynJsNcCiO8pKemnlKSu166F8fOH7n5+OXbMQdy/jHcDPwpcxV2F9SbglIi8D/jxfct4a0Uul+up8tuoV2vN5XJ7spWUUtSaDmXL7rlU4jColvKBZmQcWkqyWGelYFHtp5RkKrqTDdQ+R2AYpSSDvnbHYfz8obufX7oZkP6nwMuB71RK/W3rSRF5EW720k8Avz8UOx+0z5AuW4LtOExP7C41HVTlt/3Mzs4CYNkO1YbbdRR0+c12JjJTIzlOeynJ9qygfkpJRkKtpaNbWUGt+QGjLSU5qmvXL8bPH7r7+aWb4PAjwE+1BwYApdTfisi/AN6BZsHh4uU877p/iWrD4VzGot6MUbbcG8xUMhJo5bcWrRbC6uY2RSeqVUBox6pUBjoLtGR5S0W0jQW0Skn2egWSEeFUJrEzFtBaRlqXUpKDvnaDxvj5Q3c/v3QTHJ4L3H/EtvuB9w1OZzC8++JVinWbUAjSEQWiEIFi3SYcCgVW+a0VECoNh1rDQaGo1OvENQ0MAM1m7xlRSily1eaBBeNWiv2NB0wlIjvZQDurh6ZjOPlVZhZPH7+DgOjn2o0S4+cP3f380k1wCCuliodtUEoVRSTYdX0P4UrBQilo2HAxF6FVv8ZW8L43PW+kLg3bwbIV1YZNvekc+Hase650J79m23jAnpZA0aLex3jAibbU0PaSkkeVkmxGgmv5dcP1/NnqgPELlm6CQ1REXoH799vvPkaK7Shat6bvmGpy/4Y7ttB01NCWwlDK7apq2oqm4z62ms6x3UW650oXt9aZmD3JSrF+oIhM0KUkr4drZ/z6x/gFSzc39jXgPcds15ZNa+8NZ1BLYbRaBPWmg2U72M7+OmrdEY3rkw5XqDdZ2ZcVdG27ylbt8Z73lYqG9mQFtUpKzg6wlKRO1+4wjJ8/jF+wHBsclFK3+D2IiLwHdyXXNaXUbd5zM8BHgFuAbwE/qJTK+T0WsKcQSrG590bUz1IYSikatsKyHep2dy2CbglHose/aIC4pSSbXgtgb2ugnxVop5ORnTGA9jkCoyglOepr1yvGzx/GL1hG1SX0XuBe4A/bnvt54C+VUr8hIj/v/f5zgzhYWNjp7rhlwmapsttnfVz6qqPcrqGG4waDhq1o2AfHCgZFrVwkOdl5VdZ+aDqK1ZLFyr6soOVivedSkkJr6ei9pSQXO4wHjIJhXbtBYfz8YfyCZSTBQSn1gIjcsu/p1+HOnwD4A+CzDCE4PFbce4qv+bZZCrUmjlIo5bYyHJQ7TqHoqnTmIEllj6/n0In2UpLtJSXX+xwPyCYiFGpNomEhEQmRCdvk7TBvev4Jzp08vmrdKPF77YaN8fOH8QuWIAeTF5RSy97jFWBhUDuORcI0LHcNopsnbNbq7rhDLAzPnJ2gUO99ueVhUSsViSU6lxtUSlGs214h+d2MoGuF/ktJHswKcktJvuv+pwgLxL2lRp6dbPDlQoRPPr418uDw8HKRTz6+xXq5wXwqyl23zuxx6ObaBYnx84fxCxYtMo2UUkpEDv2eu7a2xoULF4hEIti2zd13380999zDysoKqVSKcDhMoVBgfn6era0tlFJMxxR3ZCxW6iGelrSZjCge2o7wwmmH/PoKE5kpStubJCYmcRwHq1YhPTNPcWudcDhCYjJNOZ8jkUpjNxs06rWd7ZFIjNjEBJXCNsnJLA2rRtOq726PxYnGElRLeSYyU1iVCs2mtbM9Gk8QjkSplYukstNUinlsu0l6Zp785holFWet6nAlV2arGeXqdpXVikO12XuLZiYZYS6hOJmKcHomxZTUeNpchmTI8c5p1jsnh1hIyK+tEW5UeW7aYTLs8LVqgpsTdRoKVqtNcqtXuzqnWqm4c07FrXViiQlCoRC1SonJqVkqhW2UUqSmZijlNnYmEtWrZSan5yhvb7FStPj4kzVuTVlMTUYRZfH4kwXEPs3paIVwOIJVr5JbvTqSz6mfc6pVStirzZ1zEhFt/+31+zkN85zK+RwSDg/9c/JzTtFEcqif01R4gUKhQKPRYHFxseN9b35+ntXV1WPLDneLjGpZJK9b6eNtA9KPAy9XSi2LyEngs0qpW/e/78EHH1Rnz57t6Vg/+4knuFqoUarbxLGpEWYiGmJhMs7bX3788tjDpr2U5NXtCqtlt1WwUrR6Hg8IS2t+QLxtTMAtJZnoo5Tkb312ie1qY6flkAw5bNdhKhkd6bXb7wFQbzh7PJoNi0g0+CVQjsL4tfLfvf9L23PtjwHZ95qWXzQWO/R17T923rJ/nyJ7ft95Wft7OyRNdEqnEAHLsojFOl+/Xve//7mYj3Kwly5d+uL58+fv6Pf9QbYcPgb8GPAb3s8/G9SOW0tvz06E+I5Mk4vbEZoK7rp1ZlCH6Ipqw94zQ7iVFbRetuh1kdV4WLzU0PiekpLzk4MtJXnXrTO8/0ur0HCIRYRnxqt8oZYc+bVbLzdIxfaeVywirJd3K93pnmeuo1/rdikC5dwGs4s37aQWi0AIAQGv7Pruttb7254LiXvzC3nPi4h3wz3+xtsNS0vLnOqi1klQrF1b66oWy/XKSIKDiHwId/B5TkSuAL+MGxT+SEQuAEvADw7qeK2ltz/80CrL1TpTyYP91YNCKUWhbntlJOt7Skr2W0pycd8M4VOZ0ZWSPHcyzVtgp6+/lorzlhcsjHy8YT4V9VoOu+dsNRXzqd30Qb/9vYIQ8m527Zd2/02y/cYX2n8TbD12d7jnm1/UyjCbjnf85rr73O5vrda82vl97/vVEa/b84K2G/RRN+yYlWEurW9NglRK73WLdPfzy6iyld58xKbzwz52Qw3mhuooxUa5sWe9oNbPSo+lJAHmJqJuEfkEPG02vRMIJuPBDwOdO5neCQbVYp5kevRZSvtbMFZTea2/WcIihESIRcIkIiG3q8G7kYdCe2/i7Tf/8L5vvMMmFon01ao70B1y5C78nUM4rFfZ0v0Yv2AJ/k40BC5eznPv564QFviOjM3F7Qbv/9Iqb4FjvwE37NbS0dae1kA/pSTDAovpvbUDTqbdxeP2lpLUNyWuVimRTPubUS4c/Hbb+hYbEgiHxLvh737TfdnTp5mMRfjoo+usldzCS284d4IXP21qZx9LW1XmUvqur1QoFJie1vezNX7+0N3PL2MZHO57eI1oSIiFhW/W4u6gZsPZk45ZsezdZSLaWgHrPkpJtgKA1XR4dKXMdq1JOh7mpbdkjwxKk1Ozx+7/uJTOYdLuFxL3Jh4O7f122/79NRwSomEhFg4dWkaz1RXSzTf3737GNN/9jKP/+Obn9V74zPj5w/gFy1gGh1blN8dRzEUsVmtxGrbDN3M1fuuzSywX6+RrvS8VkYmHd7KB2ktKtpeSfHi5yPu/tEpEYDIeYrvaudVSKWyTnV888pjt+0vF5Nj9dUM4JERCbtdMq089JEK41SUT8r7Ri3CtuMGp+exABhgZ0D5abG1tMTGhb5658fOH8QuWsQwOC5NRvrJWpunANwgBuxkuX12vdHyvAHNeKcn2rKCTmTipLkpJfvLxLSJtk8jiXr3qoyaRHZdK3Ov+9tz4vRt82LvZR7xtPd2gldKicM5haFydFjB+fjF+wTKWweH5pyZ5eKXc8TX7S0m2fi6mY8R8lJLsJgWzndRU5xTR/fsT3G6sjXKDTDzi9td7N/3wEAZadW466+wGxs8vxi9YxjI4PLxcJhoSGo4iLApHuf3g2USEN9++wMlMnPkhlZLsJgWznVJug+kFN9c8EhKi3s2+9W3/VCZGrtIgEd39xl9rONyUjZNJDP/jW11d1TaXW2c3MH5+MX7Bol0Vt0GwUrSYSoaJh+FZEzaxiJBNhHEU3H4qzcJkbGhdJXfdOkNTubN5lVLUG87OBLywuAO1qWiYqUSEuVSMxZkpbsq49Q5OTMaYnoiSSURIxcIko2F+6PkL7v6ayi0z2nBGWv96UFPxh4HObmD8/GL8gmUsWw4TsRBP5Sx3oDXkVobbLDc5lR3+Uga3n8oQDYf488c2WSt7KZjffoIXPy17aJdP+ZiuoNaEvvseXmOlaAVW/9pgMNxYjGVwQLlV2WwHTsQdHit66ZZDGEAKh4REOEQ84v4XDgmnMnG+79nHp6gClEolZmc7v/bOM9nAgkE3fkGhsxsYP78Yv2AZy+CwVWnuzLp6KL97itvV3tNXWwjuuEUrhz/i5fP77Z5aWBjYSuVDQWc/nd3A+PnF+AXLWI452Aok5NZveH62STTs5vJ3s8hFWIR4JEQqFmY6GWUu5c5puCnrjQkko6RiYeKR0EDGLdbX133vY5jo7KezGxg/vxi/YBnLlkMkLNBwg4GtxO1NUhxY50aAeCRELBzqOKt3mIxijR8/6OynsxsYP78Yv2AZy+Bw81Rip57D48UwttdkaDqKR1ZK/IOnZUlEQu6ibUP6gC9eznc1iDwzM9qlsHtFZz+d3cD4+cX4BctYdiu98dwJIqEQE7EQt2Wa3vIQkIwIH3xolSc2KiSj4aEGhns/d4WtSoN0PMRWpcG9n7vCxcv5A6/VvWmqs5/ObmD8/GL8gmUsWw53nsnyfc8u84FLq1wWN/5l4iFmUjFqDYf7Hl4Dhpce2lr4L+EteZHwlry47+G1A8fIZDIDOeaw0NlPZzcwfn4xfsEyli2Hi5fzfPqJHCIwEYZwCIqWQ6neJB4RvrVV7fqbfT+sFC3ikb2tknhEWClaB15r2/1nUI0Cnf10dgPj5xfjFyxjGRx2luyOhJiLOzvFXrZrNvWmwlbsfLMXcX9GQ7LTovDLYjpGvbl3TkW9qVhMH5yEVy53XgMqaHT209kNjJ9fjF+wjGVwaH1zn05G+NJ2FEeBKLCa7tITkbB0/c2+H9547gQNx13q4rglLxYXj16uWwd09tPZDYyfX4xfsIxlcGh9c0/Fwrz0hEMkJNgKktEwb3vJaW6eSnT9zb4f3CUvTjMzEaVYd5iZiPK2l5w+dExjZWVlIMccFjr76ewGxs8vxi9YxnJA+o3nTnDv565Aw8GJh5ibiNJw1J4bdGt7PCLUm2rgi9l1u+RFNHr4aq26MGy/blN+g3Dzi/Hzh/ELlrFsObR/c/9mOXLgm3sv3+yHTTar9wJ6w/TrJeV31G6DwPj5w/gFy1i2HGD3m/vS0tKha64HuZhdOxsbG6RSqaA1jmSYfr2k/I7abRAYP38Yv2AZy5ZDO7pH9xvZr5eU38O4ka/dIDB+/tDdzy9j23J436Vr/OkjGzw9UeebtTivv22OH3nhqaC1DmBZg8mQGhbD9FtMx9jyqty16CUx4Ea+doPA+PlDdz+/jGXL4X2XrvHBL61SbdrMxx2qTZsPfmmV9126FrTaAarVatAKHRmmXy8pv6N2GwTGzx/GL1jGMjj86SMbIO4qrF8uRN3VWMV7XjN0z5Uepp/fxIAb+doNAuPnD939/DKW3UqVhk2rK/v52Qb/YytO2HteN1ZWVrQuUj5sv24SA45Kd73Rr51fjJ8/dPfzS+AtBxH5GRF5VEQeEZEPiUjC7z4nomFaYaDUdE/R9p7XjVhs+HWt/RC0X6d016DdjsP4+cP4BUugwUFEbgJ+CrhDKXUbEAbe5He/r79tDsdxBzcvl91JbrYDd56Z9LvrgZNOp4NW6EjQfu3prvvXwQra7TiMnz+MX7AE3nLA7dpKikgEmAAGMmrcWhzjOendrqQvPFUc2Mqrg2JzczNohY4E7dcp3TVot+Mwfv4wfsESaHBQSl0F3gU8BSwDeaXUp/zutzXwLMA3ymFat5ZKWy0HXZieng5aoSNB+3Va4TZot+Mwfv4wfsES6IC0iEwDrwOeDmwD94nIW5RS72+9Zm1tjQsXLhCJRLBtm7vvvpt77rmHlZUVUqkU4XCYQqHA/Pw8W1tbKKWIqgbfM9dkuRbilgmbZ6RsHspH+PZMk6QqUKlUWF9fJ5PJYNs25XKZxcVFVlZWiEajZLNZNjY2yGazWJZFtVrd2R6LxUin02xubjI9PU21WqVWq+1sTyQSJJNJcrkcs7OzFItFLMva2Z5MJonFYuTzeebm5lheXiaXy+1sP+qc5ufnWV1dZXLS7RorlUosLCywvr6OiDAzMzOUc7py5Qqzs7M9nVM+n6fRaAzknF7/NMXHn7RIhGE+ZvNoKc65VI07b55me3ubXC43ks+pn3Nq+Y3iczL/9gb/b++4c7py5QqnTp3S8t/eQO7PSqnjXzUkROSNwF1KqQve7z8KvFgp9c9ar3nwwQfV2bNne9rv3X/4MCXL7U76njmL+zfcgSMBzp2c5J2vfvZgTmAAHLW8hy7o4HdUtpIObp0wfv4wfv64dOnSF8+fP39Hv+8POpX1KeDFIjIBVIHzwN/53emdZyb5zJPu2MIXt3dPMRZiZ4KVn9VAB4nuudI6+B2V7qqDWyeMnz+MX7AEPebwBeCPgUvA33s+/8nvfr+Zq+88/o6p5s7jqYkod57J+l4NdJDovia8zn46u4Hx84vxC5bAs5WUUr+slDqrlLpNKfUjSqn68e/qzJV8nUjIzWop2SHiESESgq2qGyg6pUeOmkTC97SOoaKzn85uYPz8YvyCJfDgMCxaGUq5RmjP7+B/NdBBkkwmR37MXtDZT2c3MH5+MX7BMpbB4XQmhqPAUYpbkk0cpXCU+zx0To8cNblcbuTH7AWd/XR2A+PnF+MXLGMZHC7ceRPpeJgQwtdK7s90PMyFO28C/K8GOkhmZ2dHfsxe0NlPZzcwfn4xfsEylsHhzjNZXvvcOaLhEItxh2g4xGufO6dlmdBisTjyY/aCzn46u4Hx84vxC5agU1mHwsXLeT79RI7pZIQz6TCrKsKnn8hx63xqT4DQoUyo7gVDdPbT2Q2Mn1+MX7CMZcuhPRvpyXoi0Gyk49A9V1pnP53dwPj5xfgFy1gGh/ZspGcmakBw2UjHoXuutM5+OruB8fOL8QuWsQwO7dlIRdut4RBUNtJx6J4Op7Ofzm5g/Pxi/IJlLINDezZSxQ4Fmo10HLoXDNHZT2c3MH5+MX7BMpbBoT0baSpkBZqNdBz5vF71Jfajs5/ObmD8/GL8gmUss5VgNxupXC6TSqWC1jmSubm5oBU6orOfzm5g/Pxi/IJlLFsO7ege3Y1f/+jsBsbPL8YvWMY+ODQajaAVOmL8+kdnNzB+fjF+wTL2wUH3XGTj1z86u4Hx84vxC5axDw665yIbv/7R2Q2Mn1+MX7CMfXDQeTAajJ8fdHYD4+cX4xcsY5ut1CoDGrHKNGO5wMqAHkc4HA5aoSM6++nsBsbPL8YvWMay5XDxcp533b/EV9fKZEJ1vrpW5l33LwVSBvQ4CoVC0Aod0dlPZzcwfn4xfsEylsHh3RevUqzbOCgeK0ZwUBTrNu++eDVotQPMz88HrdARnf10dgPj5xfjFyxjGRyuFCxCAiERnjVpExIhJO7zurG1tRW0Qkd09tPZDYyfX4xfsIxlcABoFQENy97fdUMpXc1cdPbT2Q2Mn1+MX7CMZXA4nY2jFDgKvlKI4ChQyn1eN3Rvmursp7MbGD+/GL9gGcvgcOFFp8gkIoSA56UbhIBMIsKFF50KWu0Aq6urQSt0RGc/nd3A+PnF+AXLWAaHO89kec23zRINh1iuhYiGQ7zm22a1TGWdnJwMWqEjOvvp7AbGzy/GL1jGMji015BeTMeYTro1pHVMZTUYDAYdGcvg0F5Deiba1LqGdKlUClqhIzr76ewGxs8vxi9YxnKG9ErRIoTiSqXBVkGoqDpT8bCWNaQXFhaCVuiIzn46u4Hx84vxC5axbDlMRIW1coOm43Bbxv25Vm4wEZWg1Q6wvr4etEJHdPbT2Q2Mn1+MX7AEHhxEZEpE/lhEvioij4nIdw5gp60H2EoA2fe8PoiGTu3o7KezGxg/vxi/YNGhW+n3gE8qpd4gIjFgwu8OK5bDicko2zWbr5UiRELC1ESEiuX4tx0wMzMzQSt0RGc/nd3A+PnF+AVLoC0HEckC3w28G0ApZSmltv3udzEdIxIKcTob57sWFKezcSKhEIvpmN9dDxzdm6Y6++nsBsbPL8YvWIJuOTwdWAf+i4g8H/gi8NNKqXLrBWtra1y4cIFIJIJt29x9993cc889rKyskEqlCIfDFAoF5ufn2draQinFG547xecfWyLXjKAcOBsv8Vgzxt03R7ly5QozMzOsr6+TyWSwbZtyuczi4iIrKytEo1Gy2SwbGxtks1ksy6Jare5sj8VipNNpNjc3mZ6eplqtUqvVdrYnEgmSySS5XI7Z2VmKxSKWZe1sTyaTxGIx8vk8c3NzNBoNlpaWdrYfdU7z8/Osrq7u5FaXSiUWFhZYX19HRIZ2TrVajdXV1Z7OKZ/P02g0hn5OsViMpaWlkXxO/ZyTiLC0tDSSz8n82xvtv71sNkutVmN7e1vLf3uDQIJcH0RE7gA+D3yXUuoLIvJ7QEEp9a9br3nwwQfV2bNne953q55DslmiGpnUtp7DxsYGc3NzQWscic5+OruB8fOL8fPHpUuXvnj+/Pk7+n1/0APSV4ArSqkveL//MfDCQez4zjNZ3vnqZ3Ph9hne+epnaxkYAMrl8vEvChCd/XR2A+PnF+MXLIEGB6XUCnBZRG71njoPfGWQx9C9CLjx6x+d3cD4+cX4BUvQLQeAfw58QEQeBm4H/t0gd657EXDj1z86u4Hx84vxC5bAg4NS6iGl1B1KqXNKqR9QSuUGuf+PfvSjg9zdwDF+/aOzGxg/vxg/f2xtbfkaEAk8OAybP/mTPwlaoSPGr390dgPj5xfj549CoeCr4MTYB4dmsxm0QkeMX//o7AbGzy/GL1gCTWXthr/8y79cB5b6ff/W1tbczMzMxgCVBorx6x+d3cD4+cX4+aNer9/6qle9Kt3v+7UPDgaDwWAYPWPfrWQwGAyG3jHBwWAwGAwHGNvgICJ3icjjIvJ1Efn5oH0ARORbIvL3IvKQiPyd99yMiHxaRJ7wfk6P0Oc9IrImIo+0PXeoj7j8B+96PiwiA5nJ3offr4jIVe8aPiQir2rb9g7P73ER+Ycj8DsjIn8lIl8RkUdF5Ke957W4hh38tLiGIpIQkYsi8mXP71e9558uIl/wPD7irdaMiMS937/ubb8lIL/3isg3267f7d7zQfyNhEXkSyLyce/3wV07pdTY/QeEgSeBZwAx4MvAczXw+hYwt++53wJ+3nv888BvjtDnu3GXK3nkOB/gVcB/wy2O8WLgCwH5/Qrwrw557XO9zzmOu6Djk0B4yH4ngRd6j9PA1zwPLa5hBz8trqF3HSa9x1HgC951+SPgTd7zvw+81Xv8z4Df9x6/CfjIkK/fUX7vBd5wyOuD+Bv534EPAh/3fh/YtRvXlsOdwNeVUt9QSlnAh4HXBex0FK8D/sB7/AfAD4zqwEqpB4CtLn1eB/yhcvk8MCUiJwPwO4rXAR9WStWVUt8Evo7772BoKKWWlVKXvMdF4DHgJjS5hh38jmKk19C7Dq1CzFHvPwV8L+46a3Dw+rWu6x8D50WGV3Gng99RjPTzFZHTwKuB/9f7XRjgtRvX4HATcLnt9yt0/qMYFQr4lIh8UUT+qffcglJq2Xu8AgRdmPYoH52u6du8Zvt72rrhAvXzmukvwP12qd013OcHmlxDr1vkIWAN+DRua2VbKdWaRNDusOPnbc8Ds6P0U7uLhP66d/1+R0Ti+/0OcR8Gvwu8HWhVMZtlgNduXIODrrxUKfVC4JXAPSLy3e0bldvm0ya3WDcfj/8beCbuOlzLwL8P1AYQkUng/wP+hVKq0L5Nh2t4iJ8211ApZSulbgdO47ZSel+ff4js9xOR24B34Hq+CJgBfm7UXiLyGmBNKfXFYR1jXIPDVeBM2++nvecCRSl11fu5Bvwp7h/Daqvp6f1cC84QOvhocU2VUqveH6wD/Gd2uz0C8RORKO6N9wNKqdZ6Ctpcw8P8dLuGntM28FfAd+J2x7QKkbU77Ph527PA5oj97vK665RSqg78F4K5ft8F/CMR+RZut/n34pZcHti1G9fg8LfAs72R+xjuAMzHghQSkZSIpFuPge8HHvG8fsx72Y8BfxaM4Q5H+XwM+FEvI+PFQL6t62Rk7OvDfT3uNWz5vcnLyng68Gzg4pBdBLfE7WNKqd9u26TFNTzKT5drKCLzIjLlPU4C34c7LvJXwBu8l+2/fq3r+gbgM17LbJR+X20L/ILbp99+/Uby+Sql3qGUOq2UugX3/vYZpdQ/ZpDXbtij6UH9h5s58DXcPsxf1MDnGbiZIF8GHm054fb7/SXwBPAXwMwInT6E263QwO2fvHCUD24Gxv/pXc+/B+4IyO993vEf9v7Bn2x7/S96fo8DrxyB30txu4weBh7y/nuVLtewg58W1xA4B3zJ83gE+KW2v5WLuAPi9wFx7/mE9/vXve3PCMjvM971ewR4P7sZTSP/G/GO+3J2s5UGdu3M8hkGg8FgOMC4disZDAaDwQcmOBgMBoPhACY4GAwGg+EAJjgYDAaD4QAmOBgMBoPhACY4GG4IROStIrIqIiURmRWR7xJ31dSSiPyAiHxWRP7XoD0NBl0wwcGgLeIuH/3f9j33xBHPvanDfqLAbwPfr5SaVEptAr8G3Ov9/tEevZSIPGvfc78iIu/vZT8Gg86Y4GDQmQeAl4hIGHZm9kaBF+x77lnea49iAXcS0KNtz92873eDwdCGCQ4Gnflb3GBwu/f7y3CXB3h833NPAv9QRB4TkaKIfENEfgJARJ7jvR5gW0Q+IyKtWh//1etWaq2quYOI/Li3v5yI/HcRubkXcRF5iYj8rYjkvZ8vadv2LRH5n9p+32l1iFtg5v0isiki2957F7xtWRF5t4gsi1us59+2Bclnicj93vE2ROQjvfgaDPsxwcGgLcqtxfEF3KI/eD//Gvibfc89gLu43WuADPBPgN8RkRcqpb4GPM977ZRS6nuVUs8EngJe63Ur1duPKyKvA34BuBuY9475oW69RWQG+ATwH3CX0vht4BMi0s3y0j+GuyjaGe+9PwlUvW3vBZq4LaUX4K7P1Ron+TfAp4Bp3AXX/mO3vgbDYZjgYNCd+9kNBC/DvVH/9b7n7ldKfUIp9aRyuR/3RvmyPo/5k8D/oZR6TLlr3/874PZ9rYdL3jf7bRHZxq341uLVwBNKqfcppZpKqQ8BXwVe28WxG7hB4VnKXTn1i0qpgtd6eBXusttl5a7s+zu4i6613nczcEopVVNK/U2f524wACY4GPTnAeCl3rfxeaXUE8DncMciZoDbgAdE5JUi8nkR2fJu1q8C5vo85s3A77Xd+LdwF1VrL9zyQqXUVOs/4Dfatp0Clvbtc4nuCr+8D/jvwIdF5JqI/JY3oH4zbhfbcpvX/wOc8N73ds/xorj1jn+8+9M1GA4SOf4lBkOgPIjbzfK/Af8DwPsmfc177pr336PAjwJ/ppRqiMhHcW+W/XAZ+HWl1Af6fP813Jt5O08DPuk9LgMTbdsWWw+UUg3gV4FfFbd625/jjpn8OVDHrUHeZB9KqRXc64GIvBT4CxF5QCn19T7PwXCDY1oOBq1RSlWBv8MtpP7XbZv+xnvuASAGxIF1oCkir8Ttj++X3wfeISLPg52B4Df28P4/B54jIj8sIhER+SHgucDHve0P4dZNiIrIHeyuv4+IvEJEvt0baC7gdhc5yq0L8Cng34tIRkRCIvJMEfke731vFLemMEAOd6nuVvlIg6FnTHAwXA/cj9t90t6P/tfecw8opYrATwF/hHtj/GF8FHdSSv0p8Ju4XTsF3HX7X9nD+zdxB8f/JW61rbcDr1FKbXgv+de4ZTpzuK2ED7a9fRG3AHwBt/DN/bhdTeC2jGLAV7z3/jHQKtzzIuALIlLCPfefVkp9o/uzNhj2Yuo5GAwGg+EApuVgMBgMhgOY4GAwGAyGA5jgYDAYDIYDmOBgMBgMhgOY4GAwGAyGA5jgYDAYDIYDmOBgMBgMhgOY4GAwGAyGA5jgYDAYDIYD/P/FXQaqqlJvcAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(x=\"WaffleHouses\", y=\"Divorce\", data=dset);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is an example of a spurious association. We do not expect the number of Waffle Houses in a state to affect the divorce rate, but it is likely correlated with other factors that have an effect on the divorce rate. We will not delve into this spurious association in this tutorial, but the interested reader is encouraged to read Chapters 5 and 6 of [[1](#References)] which explores the problem of causal association in the presence of multiple predictors.\n",
+    "\n",
+    "\n",
+    "For simplicity, we will primarily focus on marriage rate and the median age of marriage as our predictors for divorce rate throughout the remaining tutorial.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <a id='#Regression-Model-to-Predict-Divorce-Rate'>Regression Model to Predict Divorce Rate</a>\n",
+    "\n",
+    "\n",
+    "Let us now write a regression model in *Pyro* to predict the divorce rate as a linear function of marriage rate and median age of marriage in each of the states.\n",
+    "\n",
+    "First, note that our predictor variables have somewhat different scales. It is a good practice to standardize our predictors and response variables to mean `0` and standard deviation `1`, which should result in [faster inference](https://mc-stan.org/docs/2_19/stan-users-guide/standardizing-predictors-and-outputs.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardize = lambda x: (x - x.mean()) / x.std()\n",
+    "\n",
+    "dset[\"AgeScaled\"] = dset.MedianAgeMarriage.pipe(standardize)\n",
+    "dset[\"MarriageScaled\"] = dset.Marriage.pipe(standardize)\n",
+    "dset[\"DivorceScaled\"] = dset.Divorce.pipe(standardize)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We write the Pyro model as follows. While the code should largely be self-explanatory, take note of the following:\n",
+    "\n",
+    "- In Pyro, model code is any Python callable which can optionally accept additional arguments and keywords. For HMC which we will be using for this tutorial, these arguments and keywords remain static during inference, but we can reuse the same model to generate [predictions](#Posterior-Predictive-Distribution) on new data.\n",
+    "- In addition to regular Python statements, the model code also contains primitives like `sample`. These primitives can be interpreted with various side-effects using effect handlers. For more on effect handlers, refer to [[3](#References)], [[4](#References)]. For now, just remember that a `sample` statement makes this a stochastic function that samples some latent parameters from a *prior distribution*. Our goal is to infer the *posterior distribution* of these parameters conditioned on observed data.\n",
+    "- The reason why we have kept our predictors as optional keyword arguments is to be able to reuse the same model as we vary the set of predictors. Likewise, the reason why the response variable is optional is that we would like to reuse this model to sample from the posterior predictive distribution. See the [section](#Posterior-Predictive-Distribution) on plotting the posterior predictive distribution, as an example.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model(marriage=None, age=None, divorce=None):\n",
+    "    a = pyro.sample(\"a\", dist.Normal(0.0, 0.2))\n",
+    "    M, A = 0.0, 0.0\n",
+    "    nums = 0\n",
+    "    if age is not None:\n",
+    "        nums = age.shape[0]\n",
+    "        bA = pyro.sample(\"bA\", dist.Normal(0.0, 0.5))\n",
+    "        A = bA * age\n",
+    "    if marriage is not None:\n",
+    "        nums = marriage.shape[0]\n",
+    "        bM = pyro.sample(\"bM\", dist.Normal(0.0, 0.5))\n",
+    "        M = bM * marriage\n",
+    "    sigma = pyro.sample(\"sigma\", dist.Exponential(1.0))\n",
+    "    mu = a + M + A\n",
+    "    with pyro.plate(\"data\"):\n",
+    "        pyro.sample(\"obs\", dist.Normal(mu, sigma), obs=divorce)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <a id='Model-1:-Predictor---Marriage-Rate'>Model 1: Predictor - Marriage Rate</a>\n",
+    "\n",
+    "\n",
+    "We first try to model the divorce rate as depending on a single variable, marriage rate. As mentioned above, we can use the same `model` code as earlier, but only pass values for `marriage` and `divorce` keyword arguments. We will use the No U-Turn Sampler (see [[5](#References)] for more details on the NUTS algorithm) to run inference on this simple model.\n",
+    "\n",
+    "The Hamiltonian Monte Carlo (or, the NUTS) implementation in Pyro takes in a potential energy function. This is the negative log joint density for the model. Therefore, for our model description above, we need to construct a function which given the parameter values returns the potential energy (or negative log joint density). Additionally, the verlet integrator in HMC (or, NUTS) returns sample values simulated using Hamiltonian dynamics in the unconstrained space. As such, continuous variables with bounded support need to be transformed into unconstrained space using bijective transforms. We also need to transform these samples back to their constrained support before returning these values to the user. Thankfully, this is handled on the backend for us, within a convenience class for doing [MCMC inference](https://docs.pyro.ai/en/stable/mcmc.html) that has the following methods:\n",
+    "\n",
+    "   - `run(...)`: runs warmup, adapts steps size and mass matrix, and does sampling using the sample from the warmup phase.\n",
+    "   - `summary()`: print diagnostic information like quantiles, effective sample size, and the Gelman-Rubin diagnostic.\n",
+    "   - `get_samples()`: gets samples from the posterior distribution.\n",
+    "\n",
+    "Note:\n",
+    "\n",
+    " - We run inference with the `NUTS` sampler. To run vanilla HMC, we can instead use the [HMC](https://docs.pyro.ai/en/stable/mcmc.html#hmc) class.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run NUTS\n",
+    "kernel = NUTS(model)\n",
+    "num_samples = 2000 if not smoke_test else 2\n",
+    "warmup_steps=200 if not smoke_test else 2\n",
+    "mcmc = MCMC(kernel, num_samples=num_samples, warmup_steps=warmup_steps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sample: 100%|██████████| 2200/2200 [00:10, 202.20it/s, step size=8.56e-01, acc. prob=0.906]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         a      0.00      0.11      0.00     -0.17      0.19   2707.47      1.00\n",
+      "        bM      0.35      0.12      0.35      0.14      0.55   1621.67      1.00\n",
+      "     sigma      0.95      0.10      0.94      0.79      1.11   1390.63      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "mcmc.run(\n",
+    "    marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float), \n",
+    "    divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    ")\n",
+    "mcmc.summary()\n",
+    "samples_1 = mcmc.get_samples()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### <a id='Posterior-Distribution-over-the-Regression-Parameters'>Posterior Distribution over the Regression Parameters</a>\n",
+    "\n",
+    "We notice that the progress bar gives us online statistics on the acceptance probability, step size and number of steps taken per sample while running NUTS. In particular, during warmup, we adapt the step size and mass matrix to achieve a certain target acceptance probability which is 0.8, by default. We were able to successfully adapt our step size to achieve this target in the warmup phase.\n",
+    "\n",
+    "During warmup, the aim is to adapt hyper-parameters such as step size and mass matrix (the HMC algorithm is very sensitive to these hyper-parameters), and to reach the typical set (see [[6](#References)] for more details). If there are any issues in the model specification, the first signal to notice would be low acceptance probabilities or very high number of steps. We use the sample from the end of the warmup phase to seed the MCMC chain (denoted by the second sample progress bar) from which we generate the desired number of samples from our target distribution.\n",
+    "\n",
+    "At the end of inference, Pyro prints the mean, std and 90% CI values for each of the latent parameters. Note that since we standardized our predictors and response variable, we would expect the intercept to have mean 0, as can be seen here. It also prints other convergence diagnostics on the latent parameters in the model, [effective sample size](https://docs.pyro.ai/en/1.8.0/ops.html?highlight=effective_sample_size#pyro.ops.stats.effective_sample_size) and the [gelman rubin diagnostic](https://docs.pyro.ai/en/1.8.0/ops.html?highlight=gelman%20rubin#pyro.ops.stats.gelman_rubin) ($\\hat{R}$). The value for these diagnostics indicates that the chain has converged to the target distribution. In our case, the \"target distribution\" is the posterior distribution over the latent parameters that we are interested in. Note that this is often worth verifying with multiple chains for more complicated models. In the end, `samples_1` is a collection (in our case, a `dict` since `init_samples` was a `dict`) containing samples from the posterior distribution for each of the latent parameters in the model.\n",
+    "\n",
+    "To look at our regression fit, let us plot the regression line using our posterior estimates for the regression parameters, along with the 90% Credibility Interval (CI). Note that the [hpdi](https://docs.pyro.ai/en/stable/ops.html?highlight=hpdi#pyro.ops.stats.hpdi) function in Pyro's `Statistical Utilities` module can be used to compute CI. In the functions below, note that the collected samples from the posterior are all along the leading axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_regression(x, y_mean, y_hpdi):\n",
+    "    # Sort values for plotting by x axis\n",
+    "    idx = np.argsort(x)\n",
+    "    marriage = x[idx]\n",
+    "    mean = y_mean[idx]\n",
+    "    hpdi = y_hpdi[:, idx]\n",
+    "    divorce = dset.DivorceScaled.values[idx]\n",
+    "\n",
+    "    # Plot\n",
+    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n",
+    "    ax.plot(marriage, mean)\n",
+    "    ax.plot(marriage, divorce, \"o\")\n",
+    "    ax.fill_between(marriage, hpdi[0], hpdi[1], alpha=0.3, interpolate=True)\n",
+    "    return ax\n",
+    "\n",
+    "# Compute empirical posterior distribution over mu\n",
+    "posterior_mu = (\n",
+    "    torch.unsqueeze(samples_1[\"a\"], -1)\n",
+    "    + torch.unsqueeze(samples_1[\"bM\"], -1) * dset.MarriageScaled.values\n",
+    ")\n",
+    "mean_mu = torch.mean(posterior_mu, axis=0)\n",
+    "hpdi_mu = stats.hpdi(posterior_mu, 0.9)\n",
+    "ax = plot_regression(dset.MarriageScaled.values, mean_mu, hpdi_mu)\n",
+    "ax.set(\n",
+    "    xlabel=\"Marriage rate\", ylabel=\"Divorce rate\", title=\"Regression line with 90% CI\" \n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see from the plot, that the CI broadens towards the tails where the data is relatively sparse, as can be expected.\n",
+    "\n",
+    "#### <a id='Prior-Predictive-Distribution'>Prior Predictive Distribution</a>\n",
+    "\n",
+    "Let us check that we have set sensible priors by sampling from the prior predictive distribution. Pyro provides a handy [Predictive](https://docs.pyro.ai/en/stable/inference_algos.html?highlight=Predictive#module-pyro.infer.predictive) utility for this purpose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pyro.infer import Predictive\n",
+    "\n",
+    "prior_predictive = Predictive(model, num_samples=100)\n",
+    "prior_predictions = prior_predictive(marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float))[\n",
+    "    \"obs\"\n",
+    "]\n",
+    "mean_prior_pred = torch.mean(prior_predictions, axis=0)\n",
+    "hpdi_prior_pred = stats.hpdi(prior_predictions, 0.9)\n",
+    "\n",
+    "ax = plot_regression(dset.MarriageScaled.values, mean_prior_pred, hpdi_prior_pred)\n",
+    "ax.set(xlabel=\"Marriage rate\", ylabel=\"Divorce rate\", title=\"Predictions with 90% CI\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <a id='Posterior-Predictive-Distribution'>Posterior Predictive Distribution</a>\n",
+    "\n",
+    "Let us now look at the posterior predictive distribution to see how our predictive distribution looks with respect to the observed divorce rates. To get samples from the posterior predictive distribution, we need to run the model by substituting the latent parameters with samples from the posterior. Note that by default we generate a single prediction for each sample from the joint posterior distribution, but this can be controlled using the `num_samples` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Mean Predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>0.024132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>0.511163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>0.040832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>0.559602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>-0.124215</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Location  Mean Predictions\n",
+       "0     Alabama          0.024132\n",
+       "1      Alaska          0.511163\n",
+       "2     Arizona          0.040832\n",
+       "3    Arkansas          0.559602\n",
+       "4  California         -0.124215"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictive = Predictive(model=model, posterior_samples=samples_1)\n",
+    "predictions = predictive(marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float))[\"obs\"]\n",
+    "df = dset.filter([\"Location\"])\n",
+    "df[\"Mean Predictions\"] = torch.mean(predictions, axis=0)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### <a id='Predictive-Utility-With-Effect-Handlers'>Predictive Utility With Effect Handlers</a>\n",
+    "\n",
+    "To remove the magic behind `Predictive`, let us see how we can use Poutine [(Effect Handlers)](https://docs.pyro.ai/en/stable/poutine.html?highlight=effect%20handler). Unlike NumPyro, where we could combine the effect handlers with the [vmap](https://github.com/google/jax#auto-vectorization-with-vmap) JAX primitive to implement our own simplified predictive utility function that can do vectorized predictions, here we use a native for-loop to show the same implementation. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(post_samples, model, *args, **kwargs):\n",
+    "    conditioned_model = poutine.condition(model, post_samples)\n",
+    "    model_trace = poutine.trace(conditioned_model).get_trace(*args, **kwargs)\n",
+    "    return model_trace.nodes[\"obs\"][\"value\"]\n",
+    "\n",
+    "def predict_fn(post_samples):\n",
+    "    with pyro.plate(\"samples\", num_samples):\n",
+    "        return predict(post_samples, model, marriage=torch.reshape(torch.tensor(dset.MarriageScaled.values, \n",
+    "                                            dtype=torch.float), (-1, 1)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the use of the `condition` and `trace` effect handlers in the `predict` function. \n",
+    "\n",
+    "- The `condition` effect handler conditions the latent sample sites to certain values. In our case, we are conditioning on values from the posterior distribution returned by MCMC.\n",
+    "- The `trace` effect handler runs the model and records the execution trace within an `OrderedDict`. This trace object contains execution metadata that is useful for computing quantities such as the log joint density.\n",
+    "\n",
+    "It should be clear now that the `predict` function simply runs the model by substituting the latent parameters with samples from the posterior (generated by the `mcmc` function) to generate predictions. Each draw from the posterior can be used to get predictions over all the 50 states. We get a `predictions_1` array of shape `(num_samples, 50)`. We can then compute the mean and 90% CI of these samples to plot the posterior predictive distribution. We note that our mean predictions are similar to those obtained from the Predictive utility class.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Location</th>\n",
+       "      <th>Mean Predictions</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>-0.015601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>0.555437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>0.066771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>0.614480</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>-0.110963</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Location  Mean Predictions\n",
+       "0     Alabama         -0.015601\n",
+       "1      Alaska          0.555437\n",
+       "2     Arizona          0.066771\n",
+       "3    Arkansas          0.614480\n",
+       "4  California         -0.110963"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions_1 = predict_fn(samples_1)\n",
+    "\n",
+    "mean_pred = torch.mean(predictions_1, axis=1)\n",
+    "df = dset.filter([\"Location\"])\n",
+    "df[\"Mean Predictions\"] = mean_pred\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hpdi_pred = stats.hpdi(predictions_1, 0.9)\n",
+    "ax = plot_regression(dset.MarriageScaled.values, mean_pred, hpdi_pred)\n",
+    "ax.set(xlabel=\"Marriage rate\", \n",
+    "        ylabel=\"Divorce rate\", \n",
+    "        title=\"Predictions with 90% CI\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have used the same `plot_regression` function as earlier. We notice that our CI for the predictive distribution is much broader as compared to the last plot due to the additional noise introduced by the `sigma` parameter. Most data points lie well within the 90% CI, which indicates a good fit.\n",
+    "\n",
+    "#### <a id='Model-Predictive-Density'>Model Predictive Density</a>\n",
+    "\n",
+    "Likewise, making use of effect-handlers, we can also compute the log likelihood for this model given the dataset, and the log posterior predictive density [[6](#References)] which is given by \n",
+    "\n",
+    "\n",
+    "$$ log \\prod_{i=1}^{n} \\int p(y_i | \\theta) p_{post}(\\theta) d\\theta \n",
+    "\\approx \\sum_{i=1}^n log \\frac{\\sum_s p(\\theta^{s})}{S} \\\\\n",
+    "= \\sum_{i=1}^n (log \\sum_s p(\\theta^{s}) - log(S))\n",
+    "$$\n",
+    "\n",
+    "Here, $i$ indexes the observed data points $y$ and $s$ indexes the posterior samples over the latent parameters $\\theta$. If the posterior predictive density for a model has a comparatively high value, it indicates that the observed data-points have higher probability under the given model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_likelihood(params, model, *args, **kwargs):\n",
+    "    model = poutine.condition(model, params)\n",
+    "    model_trace = poutine.trace(model).get_trace(*args, **kwargs)\n",
+    "    obs_node = model_trace.nodes[\"obs\"]\n",
+    "    return obs_node[\"fn\"].log_prob(obs_node[\"value\"])\n",
+    "\n",
+    "def log_pred_density_helper(params, model, n, var_not_used=None, *args, **kwargs):\n",
+    "    single_param = {}\n",
+    "    tmp_tensor = torch.empty(size=(num_samples, 50))\n",
+    "    for i in range(n):\n",
+    "        if var_not_used == 'age':\n",
+    "            single_param = {\"a\": params[\"a\"][i], \"bM\": params[\"bM\"][i], \"sigma\": params[\"sigma\"][i]}\n",
+    "        elif var_not_used == 'marriage':\n",
+    "            single_param = {\"a\": params[\"a\"][i], \"bA\": params[\"bA\"][i], \"sigma\": params[\"sigma\"][i]}\n",
+    "        else:\n",
+    "            single_param = {\"a\": params[\"a\"][i], \"bM\": params[\"bM\"][i], \"bA\": params[\"bA\"][i], \"sigma\": params[\"sigma\"][i]}\n",
+    "        tmp_tensor[i] = log_likelihood(single_param, model, *args, **kwargs)\n",
+    "    return tmp_tensor\n",
+    "\n",
+    "\n",
+    "\n",
+    "def log_pred_density(params, model, var_not_used=None, *args, **kwargs):\n",
+    "    n = list(params.values())[0].shape[0]\n",
+    "    log_lk_vals = log_pred_density_helper(params, model, n, var_not_used, *args, **kwargs)\n",
+    "    return (torch.logsumexp(log_lk_vals, 0) - np.log(n)).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Log posterior predictive density: -66.72371673583984\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    \"Log posterior predictive density: {}\".format(\n",
+    "        log_pred_density(samples_1, \n",
+    "                        model, \n",
+    "                        var_not_used='age',\n",
+    "                        marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float),\n",
+    "                        divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    "                    )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial, we would like to emphasize that there is nothing magical about utility functions available in Pyro, and you can roll out your own inference utilities using Pyro's effect handling stack."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### <a id='SVI'>Regression with Pyro's Stochastic Variational Inference (SVI)</a>\n",
+    "\n",
+    "The objective of variational inference is to pick a family of distributions with its own parameters to approximate the true posterior distribution. Then we find the parameter values which make the approximate distribution close the the true posterior.\n",
+    "We carry out the steps below to define the model, extract the samples and plot the regression line. The only notable difference is that we define a `guide` to approximate the posterior. For this we use one of Pyro's [autoguides](https://docs.pyro.ai/en/stable/infer.autoguide.html) - `AutoDiagonalNormal`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "step:     0, ELBO loss: 102.63\n",
+      "step:  1000, ELBO loss: 87.01\n",
+      "step:  2000, ELBO loss: 76.31\n",
+      "step:  3000, ELBO loss: 75.36\n",
+      "step:  4000, ELBO loss: 77.44\n",
+      "step:  5000, ELBO loss: 73.24\n",
+      "step:  6000, ELBO loss: 73.90\n"
+     ]
+    }
+   ],
+   "source": [
+    "pyro.set_rng_seed(1)\n",
+    "\n",
+    "from pyro.infer import SVI, Trace_ELBO\n",
+    "from pyro.infer.autoguide import AutoDiagonalNormal\n",
+    "\n",
+    "learing_rate = 1e-4\n",
+    "\n",
+    "guide = AutoDiagonalNormal(model)\n",
+    "\n",
+    "num_iter = 7000 if not smoke_test else 2\n",
+    "\n",
+    "svi = SVI(\n",
+    "    model,\n",
+    "    guide,\n",
+    "    optim.Adam({\"lr\": learing_rate}),\n",
+    "    loss=Trace_ELBO()\n",
+    ")\n",
+    "\n",
+    "loss = np.empty(num_iter)\n",
+    "\n",
+    "pyro.clear_param_store()\n",
+    "for step in range(num_iter):\n",
+    "    loss[step] = svi.step(\n",
+    "        marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float),\n",
+    "        divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    "    )\n",
+    "    if step % 1000 == 0:\n",
+    "        print(f'step: {step:>5}, ELBO loss: {loss[step]:.2f}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the loss curve\n",
+    "plt.plot(loss)\n",
+    "plt.xlabel('step')\n",
+    "plt.ylabel('ELBO Loss');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We sample from the posterior distribution of the regression parameters of the model using the `Predictive` utility."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "posterior_svi = Predictive(model, guide=guide, num_samples=num_samples)\n",
+    "samples_1_svi = {k: v.reshape(num_samples)\n",
+    "                   for k, v in posterior_svi(\n",
+    "                       marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float),\n",
+    "                       divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    "                   ).items()\n",
+    "                   if k != \"obs\"}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us look at the regression fit of the `SVI` model. We plot the regression line using our posterior estimates for the regression parameters, along with the 90% Credibility Interval (CI). We can see that the regression fit is very similar to what we got for the `NUTS` model earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "posterior_mu_svi = (\n",
+    "    torch.unsqueeze(samples_1_svi[\"a\"], -1)\n",
+    "    + torch.unsqueeze(samples_1_svi[\"bM\"], -1) * dset.MarriageScaled.values\n",
+    ")\n",
+    "mean_mu_svi = torch.mean(posterior_mu_svi, axis=0)\n",
+    "hpdi_mu_svi = stats.hpdi(posterior_mu_svi, 0.9)\n",
+    "ax = plot_regression(dset.MarriageScaled.values, mean_mu_svi, hpdi_mu_svi)\n",
+    "ax.set(\n",
+    "    xlabel=\"Marriage rate\", ylabel=\"Divorce rate\", title=\"Regression line with 90% CI\" \n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <a id='Model-2:-Predictor---Median-Age-of-Marriage'>Model 2: Predictor - Median Age of Marriage</a>\n",
+    "\n",
+    "We will now model the divorce rate as a function of the median age of marriage. The computations are mostly a reproduction of what we did for Model 1. Notice the following:\n",
+    "\n",
+    "- Divorce rate is inversely related to the age of marriage. Hence states where the median age of marriage is low will likely have a higher divorce rate.\n",
+    "- We get a higher log likelihood as compared to Model 2, indicating that median age of marriage is likely a much better predictor of divorce rate.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run NUTS\n",
+    "kernel = NUTS(model)\n",
+    "num_samples = 2000 if not smoke_test else 2\n",
+    "warmup_steps=200 if not smoke_test else 2\n",
+    "mcmc = MCMC(kernel, num_samples=num_samples, warmup_steps=warmup_steps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sample: 100%|██████████| 2200/2200 [00:12, 174.82it/s, step size=7.52e-01, acc. prob=0.921]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         a     -0.00      0.10      0.00     -0.16      0.16   1865.24      1.00\n",
+      "        bA     -0.57      0.11     -0.57     -0.76     -0.40   1618.48      1.00\n",
+      "     sigma      0.82      0.09      0.82      0.67      0.95   1355.84      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "mcmc.run(\n",
+    "    age=torch.tensor(dset.AgeScaled.values, dtype=torch.float), \n",
+    "    divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    ")\n",
+    "mcmc.summary()\n",
+    "samples_2 = mcmc.get_samples()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "posterior_mu = (\n",
+    "    torch.unsqueeze(samples_2[\"a\"], -1)\n",
+    "    + torch.unsqueeze(samples_2[\"bA\"], -1) * dset.AgeScaled.values\n",
+    ")\n",
+    "mean_mu = torch.mean(posterior_mu, axis=0)\n",
+    "hpdi_mu = stats.hpdi(posterior_mu, 0.9)\n",
+    "ax = plot_regression(dset.AgeScaled.values, mean_mu, hpdi_mu)\n",
+    "ax.set(\n",
+    "    xlabel=\"Median marriage age\", \n",
+    "    ylabel=\"Divorce rate\", \n",
+    "    title=\"Regression line with 90% CI\" \n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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focYnQSnU+CTZT/wZhbvvXfaebLUw3UbSQ/UwuvNwi1at0xzaAZhApe1rOJ37yy9DLr/ixbzx+gpS+TIvTaUb7lMwMTHR0PvaDbfoBPdo1TrNoR2ACSRgX61xNTFl6vVkrswr05mGhord3d2m39OOuEUnuEer1mkORzgAEdkmIveJyAsi8ryIfNhum5yGjNQe8q32OhgTys0uHKfRaNoXRzgAoAT8vlLqXOAq4HYROddmm05BFey7mYY+eCuEgiteDBqvr8FUusjBWXN2p1L1Vz1tZ9yiE9yjVes0hyOygJRSJ4AT1Z/nReRFYAvwgq2GrUAifbZ99kK2z1pZQKsxPl/A5xG29dZXymJkZGRDtrYLbtEJ7tGqdZrDEQ5gKSKyE7gEeNRmU05BZeaQ6LBtnx+48fq6bvi1OJbI4/MIm6LBdfedmppi27ZtDX1OO+EWneAerVqnORzlAESkG7gL+PdKqWWrriYnJ7ntttvw+XyUy2VuueUWbr/9dsbHx4lEIni9XpLJJENDQ8zOzqKUYmhoiImJCZQ/jMrmUYUsEulDZeYAQcJR46Ye6AJVQRVzSHe/UfXT40WC3ahsAglGUOUSKpdCRfqM7V4fEuhCZZNIqBtVKkCpcPL9vgDiC6ByKeNzChkol5ZsDyJeHyqfRsIxo9lMpby4XfwhEA+qkEG6equL0JTxczq+OCFtRtOBeS+lkQEqmQSxWIxCoUA2m2V0dJTx8XECgQA9PT3Mzc0Ri8XIZrPkcrnF7aFQiHA4TDweZ2BggPn5eQqFwuL2cDhMIBAgkUgwODhIIpGgWCwubl/vPC1MbKVSKUZGRpiamkJE6O/vZ2pqimg0SrlcJp1OLx7T7/cTi8WYnp5eU9PMzAx9fX3LNM3NzREIBDpK02rnaW5ujuHh4Y7SVOs8zc3N0d/f31Gaap2nubk5wuFwXZrWvOc6ZeGEiPiB7wD3KKX+ZOX2hx9+WO3evbuhY8+ki+yb3ngdH1XMI/71n6CdjAicNdhFf5d/1X0ymQxdXV2rbp/NFNd8f7uwns5Owi1atc5TefLJJ5/Ys2fP5bW2OWISWEQE+GvgxVo3f6dgPGW3N0oZfQQSa/QjmJqqnVoKcCyR49UZ64vi2cFaOjsNt2jVOs3hCAcAXAu8G7heRJ6u/rvJbqNWIoHOeLKoKHh5Kk0qX9sJRKPRGu9R7J/JcHQuX+Md7UktnZ2KW7RqneZwxByAUupBQOy2Y13UxgutOYVyBV6aynDucISuwPLa4uXy8qqipYpi39Tao4Z2ZKXOTsYtWrVOczhlBNAWqKL13bfsZKGC6Mry0+l0evHnXKnC3vFUx938YbnOTsctWrVOc2gHYIJGm8I7mcJCGekl7cYWGk7P50vsHU+RLTY28pnJWNupzGrc0kAc3KNV6zSHdgAm2GhTeKeSK1Z4aTK92HRmfHycmUyRFyfTi6WoG2EmXTzFCTRaoK4ZbKSx9thd93D/5W/j7k3Xcv/lb2PsrnsstMx6dLP0zkI3hbcDT+f2G00XKrw8maZcUWTKwr6pTM0exGZQKA7OZikuOdBarS1bjd/fWCrr2F33sPcjnyR3bAKUIndsgr0f+aSjnUCjWtsNrdMc2gGYQIKdXWkwmS/zzIl54mXr1joUy4oDS2oRHUvklzkEO4nFYg29b98nPkcluzwbqpLNs+8Tn7PCrKbQqNZ2Q+s0h3YAJlDZhN0mNJ18SVmuczZTYiptPPmXKopjCWekkk5PTzf0vtzxSVOvO4FGtbYbWqc5tAMwgQQjdpvQEpqh89BsbrFf8USqYFkD+43Q6FNUaEvtelCrve4E9JNxZ6FHADagys5LhSzcfS/Jm28lcdVNJG++9ZQWkY2wUZ3liiKVLy2bQC5VFPuroSCl4FDc/pTaQqGx+Yiz7vwAnvDyMJknHOSsOz9ghVlNoVGt7YbWaQ5HLARrG0rOCF0ssNAneKFV5EKfYKDhqqGAKZ35UoVMoUymWCZdNH7OlSrUSvaZy5aW/RzPFukL2zdpl8021t9h89vfAhhzAbnjk4S2DHPWnR9YfN2JNKq13dA6zaEdgAmctg5grT7BG3EA6+msKHh+IkWmUFlMHW2EI/EcvSEfRimo1rORXOrNb3+Lo2/4K9H58Z2FXgdgA05bB2C2T3Ddx11Hp1JGz2EzN/9aoapMscLhuVxdWUHzq9Qt2ghuyRkH92jVOs2hHYAZvM4aMDXSJ7guLNa5EKpS45Og1GKoqnD3vZxIFnj2xPqlJqbT1q8qDgQClh/TqbhFq9ZpDu0ATOC0aqCN9gleD6t1rhWqgpPlKI7Ec6uuFJ7NFC1fRdzT02Pp8ZyMW7RqnebQDsAERlcu5xC48XrCd96BjA6DCDI6TPjOOxqO/y+EaZKvu8WyjCKoL1SlFBxP5nl+Il0zRbSs1LJJZCuYmZmx9HhOxi1atU5zOCum4XAk5LyVwBvpE7yUpmUUYYSk1Pipi6RqhapS+TLPjafY2RdmuHv5MHfG4k5kfX19lh3L6bhFq9ZpDj0CMIEqdW6O8Xphmo1gNlRVrsD+mSyvTGeWTTTHs0XKG8g6WolbUgbBPVq1TnNoB2CGDnYAVmYUrcz4ARoKVU2nizx3IrWYAVSuYGkYKJezfzFaq3CLVq3THDoEZAKnrQOwEjNhmrVYLZQUvvMOot80P5rIlSo8P5FeXFg2nSkwELEmDOSWnHFwj1at0xx6BGACp60DsBKrMoqaEUpamvwzly1taPHZUtySMw7u0ap1mkOPAMzg69wc44VwTO4vv4yamEJGhgh98FbTE8DNWpy2QEUZKaErJ4jBqEHk9dS/qjgUClliUzvgFq1apzm0AzCBdLADgJMZRSqfbrgiqFWhpLWYqeEACuUKc9lSTcewGuFw2DKbnI5btGqd5tAhIBOoXMpuE1rCRnQ2a3HaUhK50inlI8aSeRI5o8BctlhG1bFoLB6PW2aT03GLVq3THHoEYAIJR+02oSVsRKdVoaS1UMoYBYz2GI6mWK4wmSpQrpwsGSECQZ+H80ci+L21n3MGBgYss8npuEWr1mkO7QBMoAoZJND5Q8yN6rRqcdpazGVLiw7gRLJwSv9ipYxm92sNBObn5+nudt7ivmbgFq1apzl0CMgMDmwI0xTaQOfCfb1YrjCeWr1/Qa60eqVRtzQPAfdo1TrNoR2ACTp5HcBS2knniflTn/6X8vJUhtQqpaTdkjMO7tGqdZpDOwATdPI6gKW0i85SRTE+v3b3slJF8eJkhnTh1AJzbskZB/do1TrNoR2AGXzB9ffpBNpE54lkfs2n/wVKFcULE+lTnIBbUgbBPVq1TnNoB2ACcVhDmGbRDjqNp//646C1nIBbmoeAe7RqnebQDsAEKp+224SW0A46U3lzLSnBcALPT6TYN5XhRDLP1Gx8Q01mrG5Q00wSiYTdJrQErdMczn/U2yDliuJfnpvk9IEwkYB3Q8eScMwiq5xNJ+ssV4w1BDOZIqrcxezRJN0BL91BLz1BH91BL4FV1g2sZDJVoL/LX/f+djI4OGi3CS1B6zRHxzuA7700zV17Jwn7POw5s5/LtvTgkfrrxSxF5VNIoPNrjbhJZyUQIpkvk8yXASOkFPJ76AmcdAhdfg9S45opVRSHZnOcNeSsVqG1SCQSRCKNlfdoJ7ROc3S8A7h0Sw8Xjnbz7HiK77w4zVPH5/mFcwbZHG1gorNyaiZJR+JynblihVyxwlR1VbHXAwNdfk4fOPVGP5MpMpMuWlaiulkUi0W7TWgJWqc5nD923SBbYiH+8xt28CsXDhMNejmezPP5R4/znRenSebMLXhqp/z4jaB1LqdcgclUkROrpJwejGdPqU3kNHR+fGeh1wGYQEQ4b6Sb371mG9fsiCECjx9L8qkHj/CN5yeZTNWXTdIu+fEbReuszaHZHEfnTu3EVCwrDsWd3YlK58d3FrofQAMEfR7ectYAF2/u4f79cV6cTPP0WIqnx1KcORDm2p297OwL1Yz3Aoi/8+PioHWuxbFEnmJZsat/+Xun00UGuqxtWm8lboiLg9ZpFlc5gAVGugO886IRZjNFHjqc4OmxeV6ZyfLKTJbNPQGu3dnLOcORU5uLiCsGTFrnOkykCpSUIuRb/v6Ds1miIR8+E01pWoXXu7EMuHZB6zSHS77ptenv8vML5wzye6/dzhtO66PL72FsvsA/PzfJnz90lEeOJCgsie2qQsZGa1uH1rk+M+niKWUoCmXF4Xh2o2Y1hWQyabcJLUHrNIcrRwAriQS8vPH0Pq7dGeOZsRQPHZ5jNlvi316e4f4Dca7YGuXKbVEiXb12m9oSROusi1rzvpOpIv1dRfrCzgoFDQ1Z15HNyWid5nD1CGAlAa+HK7ZF+dC123jnhSNsjQXJFiv8+OAcf/rgUb71/CTT6c4vN6uy7niKapbOAzNZyhY1rreK2Vl3TOxrnebQI4AaeEQ4dyTCOcNdHJnL89PDc7w8leHJqSJPTR3j7KEurt3Zy/beTp0sddbNq3k0R2ehrDgUz9ZcN2AX9bTI7AS0TnNoB7AGIsKOvhA7+kaZShd46GCcZ8bTvDSV4aWpDNtiQa7d2cvZQ10Nry52Ik4PARXuvteSlpPN1DmZKjIYKRELOeMrpkMjnYUOAbWYoUiAt26D33vtdl63q5ewz8PRRJ6vPTPBZx46xuPHko5fDFQvKu3cxtqFu+8l+4k/Q41PglKo8Umyn/gzCnffa/pYzdbppFDQxMSE3Sa0BK3THI5xACLyRRGZFJG9dtuyGhII0xP0seeMfn7vtdv5ubMH6A35mMkU+c6L0/zpT45w/4E4mRrNR9oJJ/c9zv3llyG3YkVuLm+8bpJm68yVKhypsXBstX3n8yUSuRKz1WJ1VuKGPrmgdZrFGeNTgy8BnwH+1mY76iLo83DV9hhXbI3ywmSanx6a48R8gfv2x3nw4ByXbOnh6u0xxy4MalfUxFRdr1sVJtoo4/MFimXFzr4QAd/qz1v7pzPVgnQGfq8woK8dTZNxzAhAKfVjwNFT+Kpwao631yNcMNrN+6/cwq2XbeLMgTDFiuKxo0n+7KdH+adnJziecHaZgJXU0ukUZKR27HPp6/WGiVqlcyZT5OkT85xI5m2bpEylUrZ8bqvROs3hGAfQDkikb/VtIpzWH+Zdl27id67eysWbuvEIPD+R5vOPjfE3Pxtj31SmLZqIrKXTbkIfvBVCKyq5hoLG61XqDRO1Ume5AofiOZ4bT5kuQmgFIyMjLf9MO9A6zeGkENCaTE5Octttt+Hz+SiXy9xyyy3cfvvtjI+PE4lE8Hq9JJNJhoaGmJ2dRSnF0NAQExMTKH8Ylc2jClkk0ofKzAGChKOozBwS6AJVQRVzSHe/USTM40WC3ahsAglGUOUSKj2LZ2Cbsd3rQwJdqGwSCXWjSgUoFZDufobKcW7eFeD67YM8cjjBE1MlDsVzHIqPMxTxcfWwhwtGIvj9flQ+jYRjqHwKKuXFzxd/CMSDKmSQrt5qzroyfk7HF+PXG9VEKX9ye1VTZe4EntjIMk0qNQu+AOILoHIp43MKGSiXlmwPIl4f+e/9gMKXvoGanEKGBgh+8Fb8115oiSb/6y6jkrl1+fHf/258V5+HyqWM8zQxWfMaUhNTVNLxRU2V2WNIpK8uTVadp/nkHM9NC4MD/QQKSXpjMcrlMtmZOCrct3ieKl3dHD4cJxaLUSgUyGazjI6OMj4+TiAQoKenh5mZGfr6+shms+RyucXtoVCIcDhMPB5nYGCA+fl5pqenOeussxgfHyccDhMIBEgkEgwODpJIJCgWi4vvX+/7tBB/TqVSjIyMMDU1hYjQ39/P1NQU0WiUcrlMOp1ePKbf7ycWizE9PW2ZpkKhsLh9QdPRo0c57bTTOkpTrfP0yiuvsGnTpro0rYU4KW9WRHYC31FKnb9y28MPP6x2797d0HFn0kX2TW+8vEElOYUnaj79Kles8MTxJI8cSSzGeXsCXq7cHuPyrT2E/c6qX9KoTjgZfln2BB4KEr7zjpbF4JM332qEf1Ygo8NEv3lyFLARnVbg8wjbe0OM9AR4fjy1bA7A5xHOHuqiWFYUypXFkiTbekMNpRwfO3aMrVu3Wma7U9E6T+XJJ598Ys+ePZfX2qZDQCaQcLSh94X8Hq7d2cuHr9vOLecNMdIdYL5Q5oevzvInPznC3S/PMJdtfVhgNRrVCdZm6TRKPWEi2JhOKyhVFAdms+wdT1FYkS5q9C9Os286w6F4jrFkgbFkgf0zjc1b9Pe7o8eD1mkOxzgAEfkq8DBwtogcE5Hb7LZpJUZIonF8HuGizT188KotvPuSUU7rD1MoKx4+kuDTPz3CXc9NnlJgzA42orPeLJ1mErjxesJ33oGMDoMIMjpccwSy0fNpFfP5MrlifWtIptNFDjVQcG5qqnV/fzvROs3hmDkApdSv2W3DekjAmqX9IsIZg12cMdjFWDLPQ4fneH4izbPjKZ4dT3F6f5hrd8Y4rT+8am+CZrIRnTIyVDv8skr2TrMI3Hj9uiEnq85nqzmRLOD3eNgSq7+taTRq72inVWid5nDMCKAtUNav9N0cDfLLF4xwx7XbuGp7lIBX2D+b5W+fHOdzjxznmRPzrc8c2oDOesMvjqAJ57NVHJnL1d3JDqBcbu/FifWidZpDOwATqGLz8vn7wn5+7uxB/sNrt7PnjD66A17GUwW+vneKf3xmoqVlJjais97wixNo5vlsBQdms5yYz9f1gJBOp1tgkf1oneZwVBbQWjghC0iVi4i3NaszSxXFMyfm+cG+WbKlCjt6Q/zaxSMtyRhqpU476RSdfq8w2hNgtCe4ajeyfD5PMFh/yKhd0TpPRWcBWUQrm6X7PMJlW6K874rNRINeDs/l+OLPxlqyiKjdmsIX7r6X5M23krjqJpI331p3Ybh207kaxbLi6FyeJ48nOTibJVc6dbSom6V3Flbp1A7ADJ7W5+sPdwe47YotDEX8TKaKfOHxMaaa3ZTGBp2NsqHqoG2ksx7KFaP20NNj8+ybzpBeUpTQ72//kU49aJ3m0A7ABBK0p9Jgb9jH+y7fzNZYkESuxOceOc5fPz7GPftm2DueYi5btLTGjF06G2Ej6w7aSacZlDLCns+eSPHCRJp4tkgsFrPbrJagdZrDMWmg7YDKJpCAPV3AugJebr1sE9/YO8ULk2mOzOWWlRoe7Q7w+tN62T0c2XBzGjt11sPSSp+s4vjU+KSxIniNaqBO12kFiZxRYtozP8mOHdsZigTwrjJP0AlMT08TiUTsNqPpWKVTOwATSNDeCyvg9fDOi0bIFMocT+Y5lshxLJHnWCLPeKrAPz47yUjVEZyzAUdgt861qFlqYhUW1iMshIWAZU7AyTqtpuzv4uBsjqNzeYa6/Yz2BAmtUZ66XdEjAHNoB2ACVS7hhGenroCXMwe7OHPQWMhULFd4cmyeBw/OMZEq8E/PTjIc8fP60/o4d8S8I3CKzlrUDPnU9UYjLLTUAThZp9UsaC1VFCeSBcbnC/SFfYz2BB3TttIKCoUmz485BKt0ds6ZbwUl+8s01MLv9XDlthiXbYny5PF5HjwUZzJd5J+fm2TogOEIzjPjCNbQ2epGKys/r9Yq40VE1tznlHIUDj2fTWGFVqVgNlNiNlOiy+9htCfIYMTf9uGhbNa5vSysxCqd2gGYQLqdXWjK5xFesy3KpVt6eGpsnp8cnGMqXeRfnpvk/gN+Xr+rj/NH13cEq+lcGX5ZLbRiFbU+bzWWVvpctRroinIUTj+fVrKW1kyxwoHZLEfmcox0BxjpCRBs0/DQ6Oio3Sa0BKt0tudZtol2yRv3eYQrtka549ptvPWcQXpDPqbTRe7aO8lfPHSMZ07Mr9msfDWdra70WXe4Z0WpiXrLUbTL+bSCerSWKorjyTxPjc2zbyrDfN45FWrrRa8DMId2AGbwtteAyecRLt8a5UPXbuMXFxxBpsjX907xFw8f4+mxVRzBKjpbXelzreMuLTXhv+lN5P7yy4sLwYD6ylG02fncECa0KmW0sXxhIm1L97KNEAgE7DahJVil00XfgI3TrtUjfR7hsq1RLt7cwzMn5vnxwTlmMkW+8fwUDxyI8/rT+rhgtHsx/ruazlZX+lz185aEe1YLS4XvvGNZ85eax2/T89kIjWitKNg3neHC0e41G9o7iZ6eHrtNaAlW6WyPs+oQjHZ/7YvXI1y6JcqHrtnGL503RH/Yx2y2xDeen+IzDx3lqePGiGA1na2u9Gll/99atPv5NEOjWotlxcvT7dHLGmBmZsZuE1qCVTr1CMAEEuqMlaNej3DJ5h4uHO3mufEUDxyIM5st8a8vTPHAwTjXbQ1zcY86pbDYQgilVVlA9XzeRsJSnXI+62EjWlP5Mgdns5w+4PwRU19fn90mtASrdGoHYAJVKnTU4iGvR7h4cw8XjHazdzzFA9XQ0LdfmefHRzNcu7OXSzf34PeeHCjW02jFStb7vI2EpTrtfK7FRrVOpop0BfJs6nF2pc1sNuuKpjBW6dQhIDOUOnORibfaqvJ3r9nK288fZigsJHJlvvfSDJ9+8CgPHZ5bbEreaOXNZrGhsFSHns+aWKD1cDzHdLpAaY0MMrvJ5dq7x0O9WKVTjwBM0Ol54x4RLtzUzXlDAV6eKfDAgTnGUwXu2TfLgwfn+Lmje9n+V1+AfGvWAdTDRsJSnX4+l2KFVqXgleksIlm6/B56gj56gl56gj7HrBvQ6wDMoR2ACVRqFomN2G1G05F0nHNHRjhnOMK+6QwPHJjjeDJP79/9w+LNf5EaJRZaTaNhKbecT7BWq1KQLlRIFwqMzxuvBX2yzCF0+T229LMeHx9nx44dLf/cVmOVzrodgIgMADcBm5RSfywimwGPUurYhq1oF3zuyDFe0CkinD0U4azBLvbPZulJxGvu3qx1AE3HLecTmq41X1LkS0Wm00Xj4zxCd9BLtOoQIgFvS8pMhEKdXd11Aat01uUAROT1wF3Az4BrgT8GzgQ+ArzVEkvaAHHJDWOlThHhjIEukqPDLV0H0Gzccj6h9VpLFcVctsRctgTk8YhRxHBhhNAT9BLwWh82CofDlh/TiVils94z8CngnUqpG4GFpYGPAq+xxIo2QeVSdpvQElbTWWvCtRwIUHzvu1phluW45XyC/VorykgnPZEssG8qwxPH5nlqbJ79MxkmUwWyxfL6B6mDeLz2KLXTsEpnvSGgnUqpH1V/XkgBKJh4f0cg4c5PL4PVdS7E2bOf/RJqYor53j5+8qZfZF/kdC7cO8kbTuujv8u5LflWVhYN/rtfJ/gLb7HbrJbgxGs3V6yQK1aYTBlhI79XGOkOsCm6enP79RgYGLDSRMdilc56b+AviMhblFL3LHntTcBzlljRJqhCBgl0/hBzLZ1LJ1wlV6LnYBw5Ps8zJ1I8N57i4s09vH5XH71hZz0b1CoZkfvjzyI+v60T2K2iHa7dYllxLJHnxHye0Z4gmxtwBPPz83R3d/4CP6t01vst/X3gOyLyXSAsIv8XI/Z/84YtaCfK7VUYq2Hq1BkL+XjrOUNct7OXBw7EeXosxZPH53lmbJ7+Lj+RgJcrtkbZPRxp+InOKmqWjMgXbM9gahltdO2WK3A8kWe86gg29QSWLUZcC90Qxhx1OQCl1CMiciHwLuCLwFHgNa7KAKK1eeOtbryyFLM6+8J+fum8Ya7b2cv9B+bYO55iKl1kKl3kUDxHxO/h4i09BDweekJeLt3c0/IUwVZXMnUa7bjmYZkj6A6yKbq+I9DrAMxRl1sVkY8opcaUUn+slLpdKfVJpdQxEfkPlljRJrSqfvxCuEKNT4JSiwuuWrXqtlGdg5EAv3zBMB+8eivnDhtlB4I+IV2s8NNDCe47EOdbL0xz974ZVIuLi62WqdSuGUxmaefeB+UKi30KDseza/ay0P0AzFFvFtAfrvL6f7HEinbB15o6KK1uvHIKG9Q50h3gnReN8N9uOI0737CT37piMxdvPhmvfORIku+8NN3SCpM1S0YEA02rZOo4WnTtNpNyBcaSBSZSq4c/dBqoOdYMAYnIQszBKyJvhGU9tE8D5i2xooVsJLQiLWogYne4wkqdIsK23hDbekP8/O5BXp3OctfeSX52bJ6BB3/K+d/5xobDXPWc01olI4K/9avuiP/Tumu3FUykCmyO1nZouiGMOda7Kv66+n8II/a/gALGgQ9ZYkWL2GhPW5VPt6SEcKsbr6ykWToDXg/njkQI+Ud5/Ivf4qx//QqqaKQANlpXyMw5XVkyopKY2JCedqJV124ryBUrJHIlYqFTb1+JRILe3t7WG9VirNK5ZghIKbVLKbUL+MrCz9V/pymlrlFKfWvDFrSQjYZWJBxrglWn0urGKytpts7T+sPc8MB38Vdv/ovk8sx/5m/WjPGuZCPntFXn0wl0mtbVwkCDg4MttsQerNJZ1xyAUuo3Lfk0m9loaEXlW7OaMnDj9fX1tG0SrdDpnZpe9fU/f+gojx1NLJagXouNnNNWnc+1aFV5bSdotZJ4pljz+kgkEjZY03qs0llvLaAo8DHg9cAgS+YClFLbLbGkBWw4tFKxZrl6PbS68coyWqBztXOR7usnni3x3ZdmuG9/nCu3x0jlyxTKFX7hnMFT6sds6Jy28HzWYqMhSVPYrNVqKgqmUkW2xJaPlIsrR5UdilU6680C+ixwKfDfgX6M2P8R4E8tsaJFbDS00o651I2wUZ31PNWudi4GPnwbv3LhMJujQTLFCvftj/P4sSTPnEjx5SdOUFzx1LfacXzXXLGuDXafz1Zme9mttRlM1ggD6XUA5qjXAbwZeLtS6ptAufr/O4F3W2JFi9hoaKWdc6nNsBGd9a5hWO1chH7ues4b6ea3X7OZ91y2iTMGTqa7HUvk+eozE8ucQK3j+G96E8Xv/XBdG+w+n63M9rJbazPIlSrMZZc/Cet1AOaoNzfMAywEnVIiEgNOAGdYYkUL2UhoRfzuqDW+EZ1rPdWul5WzzAYRdvWH2dUfZipVYDpT5NsvTLF/Jss/PD3BoXiWn989yOVbo6ccJ3nzrXXZYPf5bGW2l91am8VEqkBv+GQBwkjEHT2erdJZ7wjgGYz4P8BPMEJCfwnss8SKdkGc0fau6WxAZzOeaoe6A5wzHOHWyzbT5ffgv/cB3vfH/4Uz3v5OZt/6bvL/tuLJvl4bbD6fLc326tBrN54tUSidHBF6vV4brWkdVums96r4d8Ch6s8fBrJAL9AR2UH1ogoZu01oCRvR2cySCyM9Ad4b38ebv/kPRBNxBPBOTpP6+Kc5/E//Vm0+Ur8Ndp/PVmZ72a21WSgFk+mTcwHJZNJGa1qHVTrXDQGJiBd4D/BxAKXUJPBblnx6myFdvXab0BI2ojP0wVuXZbYYL1r3VBv60snFYwv4igU8X/g7/rTvbABe/3M3c9HffxlfcckkYQ0bnHA+W5Xt5QStzWJ8voBHhFjIx9CQO2o7WaVz3RGAUqoM/A7gjvyqNVBZdzxdbERns59qVwvvLO1X/MDpF3PPzb9GMtZndC8aGappg1vOJ3S21mJZcTie49kTKZ49eIJXpjNMpQt1rSNpV2ZnrZnUr3cS+G+BD2DE/l1MaytY2sfGdDbzqXa1iVNGhjhvJML23hB9YR99V9/CFy67knxZ8TtXbSXWU6t2ilvOJ7hFa7lSYTp9sjl9JOAhFvIRC/mIhnx4WlyGvFlYVU233jmA1wCfFpFDIvITEfnxwj9LrABE5EYReVlEXhWR/2zVca2kk4fRS3GyztUmTrt+5z38yoUjXLU9xtlDEYarrQUB/uHpcaZq5Iw7WafVuEXrSp3pQoWxZIEXJzM8fjTJi5NpxpJ50oX2XhhnVQio3hHAX1X/NYXqPMNfADcAx4DHReRbSqkXmvWZjaDScSQ2YrcZTcfJOmtV9Vytiujbzx/mq0+PMzZf4AuPj/ErFw5z+kDX4nYn67Qat2hdS2dFwVy2tJgs4PcKvSEfsbAxQli5ytzJTExMsGPHjg0fp96OYM0uRP8a4FWl1AEAEfkaRrtJRzkAp/dUtQqn66w3xBQN+XjvFZv5+t5JXpzM8PdPjXPT7kGu2Go0SHe6Titxi1YzOotltdi5DtorXGRV32OnFAnfgtFmcoFjwJU22aLpIAJeD79y4Qg/ejXOg4fm+M6L0xyazXLtzl42+dd/v8Y9pAsV0oUCY8kCXg/0BH2LDiES6Mz1BU5xAOsyOTnJbbfdhs/no1wuc8stt3D77bczPj5OJBLB6/WSTCYZGhpidnYWpRRDQ0NMTEyg/GFUNo8qZJFIHyozBwgSjqIyc0igC1QFVcwh3f3GsnmPFwl2o7IJJBhBlUuo1AyeQNjY7vUhgS5UNomEulGlApQKJ9/vCyC+ACqXMj6nkIFyacn2IOL1GXXawzGjWmOlvLhd/CEQD6qQQbp6q1kcyvg5HV980tmoJkr5k9urmirJKTweb0dp2jNcZCAQ5duvJNk7kWbvRJrtEcVVW7PsHo4g+bRjNBXue4TCF+9CTU4hw4MEbnsngTdccYomM9eeSs+dvHYdfJ42+n2qJKfweAMb1lTMzDGLEK9q8ocjdHnBW8mzbctmZiYn8Pv9xGIxpqenicViFAoFstkso6OjjI+PEwgE6OnpYWZmhr6+PrLZLLlcbnF7KBQiHA4Tj8cZGBhgfn6eQqGwuD0cDhMIBEgkEgwODpJIJCgWi4yOjnL8+HGUUmve9+oZJUire7PWNELkauBjSqm3VH+/E0Ap9YmFfR5++GG1e/fuho4/ky6yb3rjC2FUqYD4Or/jUCfrjGeLPHokyZNjSfIl49rvDfm4cnuUSzdHCfntjQOvrBAKQCi44VTaTj6nS2mVTrvDRblcjlCovvIeTz755BN79uy5vNY2p8x6PA6cKSK7RCQA/CrguGYzxpNO5+MknVbXy+8L+7nx7AF+/7U7uHFHgP6wj7lciXv2zfL//eQw33tpmpnM8iUvrarZD82rEOqkc9pMWqVzaXbRz461PrtoasqagoH19gMIYjSG/zVgQCkVE5E3A2cppT6zUSOUUiUR+V3gHsALfFEp9fxGj2s9zp0UshZn6Gxmvfygz8OVo0GuPHOQV6YzPHwkwcHZHI8eTfLY0SRnDXZx1Y4YWx59mNwnW1Szn2ZWCHXGOW0+rddZrizPLgp4jVXJvWFjdNCM7CKxaMRRr2V/CpwP/AYnV5Q8D3zQEisApdT3lFJnKaVOV0p93KrjWomEo3ab0BKcorPZ9fIlHMUjwtlDEd5z2WY+eNUWLtncg9cjvDyd4ctPnGD603/dspr90LxaSk45p83GCToL1eyiV6azPHFsnmdPzHM4nmUuW6RiUci9v9+a/g71OoC3Ab+ulHoYqAAopY5jZO+4Bj2Mbi3Nrpe/UudoT5BfOm+I33vtdt54eh/dAS+ReO0l95WJKf7rDw6wf8baImvNqhDqlHO6GlaF2Zyos1a46EQyT2YD4aKWhoCAwsp9RWQImLHEijZBAl3r79QBOEVns+vlr6azO+DlDaf1cd3OXhJDg/hq9C+ej/YC8LdPjvOR122nJ2hNQp2ZhW5mcMo5rYWVoT4n6wTrwkXRqDUjnXpHAP8MfFlEdgGIyCbgM8DXLLGiXVCdW1xqGQ7R2fR6+evo9HmEnt99LwRX9J31B/jJDb+4+PtfPnyMVyzIMlsgcOP1RL/5ZWKPfI/oN09tpNMQDjmntbA01OdgnbWoFS46Es+RyJXWDBeVy9ZMNtfrAP4AOAg8h9EH4BVgDKNHsGtQxZzdJrQEp+hsemXROnQGbrye8B8st8H3Hz/ExFXXLO6TLlb4+6fGuWffDKWK/WnVtXDKOa2FlaE+J+ush3ShwvFknhcm0svCRSvT9dPptCWfV28piALwe8DvVUM/08oJCwhaTCc21q6Fk3Q2tbJonTpr2fB7GD1pA17hwUNz3Lc/zkOHExyK5/jlC4YZ6HLWMmMnndOVWBnqc7JOsywNF430BJblN7W0KbyI/KaIXAiglJpSSikRuUhE2qop/FIaWfDTiY21a6F11kfI58Ejwut29fG+yzfTG/IxlszzuUeO8eyJeYustAYnn1MrQ31O1mklrW4K/0fAxSteO4qxWOvvLLGkRfi9wmn9Yfq7/ORLFWYzRWYzRZL5OmJqns6sB3IKWue6FO6+d9lE7cgHb+UDe17Pt1+c5vmJNHftNRrY37R7kKDPAestHXxOLZ34drBOK/H7rRlh1usAosDKlkIJjPmAtmEw4mdXfxifxxhMBX0eNkWDbIoGKZQqzGaLzGZKJPMlagW4JGhNBT6no3WuzWpZK2HgHW95I6f1z3P3yzM8fSLF0USeW84fYmusvmX7zcLp59SqUJ/TdVpFLBaz5Dj1Ppq8ALx9xWtvA160xIom4/cKZw91ceZg1+LNfyUBn4fRniDnjkS4bEsPpw+E6Q37WLq7yiZaZLG9aJ1rs1bWiohw+dYov33lFoa7/cxkivzVY2P87ZMnODibtayTk1n0Oe0spqdPTU1uhHpHAP8J+J6IvBPYD5wB7AFussSKJhMNmcvR9ns9DHcHGO4OUKoo4lkjTDSbjzTJQmchQWfrXBl+aTRc0KjOerJWhrsD/PZrtnD/gTiPHU2yfybL/pksW2NBXruzl7OGuhYLiE2mChxN5Lh4k7EKuRk4/ZxahVt0WjUCqPfO+BBGKYhfB7YBjwEfVkodXfNdHYDPIwxFAgxFAoxX0vi7u5jJFpnLFunUntOqXHJs5RgrFw01qrPerBW/18MNZw5w7c5eHjua5NEjCY4l8nz1mQmGIn6u29nLBaPd3LNvhldnsjwzluI9l29qSmVJJ59TK3GLzkLh1BanjbCuA6i2a0wBvUqpT1ryqW1KPpdldGSYgYifilLMZUvMZorEsyXH5n83RCm//j42sVb4xfQooEGdoQ/eWrNk82pZK11+Y2XxNTtiPHF8nocOzTGVLvKN56e4b3+cuZyxKvTwXI5vvzjNW88ZtN4JOPicWopLdGazWUuOs64DUEqVRWQfMICx+Mu1LM299YjQ3+Wnv8twBoncSWdQLLe3M3ByLrWVi4Ya1dlo1krA6+Hq7TGu2BrlufEUDx6aYzq9vPT0k8fn8XuEnzt7wLKKj+Dsc2olbtFp1TqAekNAXwG+IyKfxmjXuHiHU0o1rzi6wxgfH6/ZiNkjQl/YT1/Yj1KKZL68mF5aaENnoFKzjm0gbuWioY3o3EjWis8jXLK5h4s2dfPyVIafHJwjkSvx5jP7+eYLUzx6NInPK9xwRr9lTsDJ59RK3KJztXuRWep1AAtlnz+24nUFnLZhK9qEQGD9TkMistgpaFd/mGSuVE0vLS52oHI8Xud2CjUbflmTDeisZyJ6vX08IpwzHOGc4QhKKUSEkN/D156Z4KeHEoR9Xl67q7dhG5fh4HNqKS7RWc+9qB7qLQWxy5JPa3N6enpMvydabRm3sy9MKl9iNlNiJlskV3TuDLKTKypauWioUZ31TESbnaxeeNI/eyjCLecN8y97J3n0SMIyB+Dkc2olbtHZyL2oFnW7SxHxAddg9AA4BjyslCpZYkWbMDMzU1ej5dXoDvroDvrY3hciXTgZJso4zBmobHKxSbYTsWrRUKM665mI3shk9ekDhk3pYpkfvTrLNTtihP0bW+Hq9HNqFW7RudF70QL1toTcDXwbCGOUgNgG5ETkrUqptlgMZgV9fX2WHSsS8BIJeNnWGyJbNJzBTKbUsp6iayGh1qymtCqfv1Ea1VnPRPRGJqvDfg/nDkd4YTLNjw/O8ejRBFdtj3H19hhBn4dkrsxMpsBMpkhFwcWbelatbWX337jVtOratRur7kX1jgA+C3we+D8LVUBF5CPV199oiSVtQDabtawRw1LCfi9bYl62xIwKkwsjg/kl9Yk8Al0BL90BL0rBdKbQtHUIqlRo+oKaZvb7rZdGddYzEb2RyWoR4Z0XjXBkLsd9++McmM3ywIE5Hj6coKI4JeX4JwfnuOHMfi7c1L0sfdQJf+NW04pr1wlYdS+qtxTExcCfrCgB/SlOLRDX0eRyza81HvJ52BwNcv5oN5dt6eHMwTAXjEa4YluUC0a72dUf5rSBMJduibK9N0TA24RlLyVrFpmsRbP7/dZFgzrrqV5pRYXL7b0hbr1sE++7fBO7+kIUyopSRdEd8LKjN8Slm3vYFguSKpT5xvNTfPHxMcaSJ/+mjvgbt5oWXLtOwKp7Ub0jgDHg9cDSlM/X4rJ1AVbl3tZLwOdh0Fd7tt/nEbbEgmyKBpjJFDk0m7NsMVorcqmb3e+3Hpq5DsDKyeodfWHec3mYRK5E0OtZFu5RSvHsiRTff2WWo4k8n3/0OJdt6WHPGf2O+Bu3Gr0OwBz1OoA/AL4lIt8BDgM7gJ8H3mWJFW2CVbm3VuIRo1RFl9/Li5NpSxahtSKXutn9fuuh2esArG5mE6tR00pEuGhzD2cPR3jgQJxHjiT42fF5np9M81sDA/hrFA1r5d+41eh1AOaoKwSklPoWcCmwF+ip/n+ZUuqbG7agjQiF7C3puxaRgJfzR7sJWVF7fpVRh5U0vd9vPbRAZ6sI+Ty85awBfueqrZzWHyZbrPD9N/w8Jf8Kja3+G7eaDjqna2HVvajeLKCLlVJPA//Dkk9tU8JhZ6eXhXwezhuJ8NJUmnSh8RliacGXyNImIA3SCp2tZqg7wG9eOsqLk2nuDl3DPcBrf/AtehJxGB6i6/b3dOwEMHTmOa2FVfeiekNA3xeRKeCrwFeUUgct+fQ2Ix6PNyULyEoCPg/njnTz8mS6vi5nNVC5VEsyKZrZ77ceWqWz1YgI5450c8ZgFw9uejNfuvRKShWFABeMdvOGdJGBiLN6FltFp57TlVh1L6o3XrAJ+I/AbuAZEXlYRD4kIsMbtqCNGBgYsNuEuvB5hN3DEXrDjS2Ll7CznZxVdLrOgNfD9Wf0c8e127h8cxcegWfHU3zm4aP86/OTxLPF9Q/SZnT6OV3AqntRvXMAZaXUd5VS7wJGgE8Dv4yxKMw1zM87q9H3Wng9Rhe0wQae9FQh0wSLnMdGdBbuvpfkzbeSuOomkjffSuFu59ZEjIV8/PwOPx+6dhuXbjZKCDw1luLPfnqUb784RSLXOQv63XLtWnUvMvWIKCIh4BeAdwKXAz+xxIo2waomDK3CI8IZA0YP5PF5E7aXO+eGsCYN6mzLBVblEn3dfm4+b4jrdvXywIE4z55I8bNj8zx1fJ7Lt0Z57a5eeoJtXkzNJdeuVfeiukYAInKTiPw9MAn8PvAAcLpS6k2WWNEmtHodgBWICLv6w2yNBdffeeE9LsmlblRnOy6wWqp1oMvPLecPc/s1Wzl/JEJZwaNHk3z6waPcs2/GEeVIGsUt165V96J65wD+D/AycIlS6iql1KeUUuOWWNBGjI+3r+RtvSF29tWXOqZSs022xhk0qrMdF1jV0joUCfCOC0f4nau2cs5wF8WK4qHDCT714BF++MosmWL7OQK3XLtW3YvqLQd9riWf1uY4PQ10PTZFg/i8wv6ZLGqt9WK++kcLbU2DOp2wiM00a2gd6QnwqxeNMpbMc9/+OPumM/zk0ByPHUvwhtP6uGp7rCl9ipuCS67dpqeBishHlVIfr/7831fbTyn1h5ZY0gZY1YTBToYiAbwivDKdYbXKEeKSphr16lxZUdN3zRUUv/dDa5rS1PF5VqyPqEfr5miQ37hklKPVInT7Z7Pcs2+WV6azvO28IaI1ViI7Dbdcu1bdi9YKAW1d8vO2Vf5trfG+jiWRSNhtgiX0d/k5ZziCd5Wzr/Lp1hpkE/XoXJjwVeOToBRqfJLi936I/6Y3IaPDIIKMDhO+8w5LJoBrfV72E3+24SwjM+d0W2+I37xsE7920Qhdfg8HZrN89pFjvDCR2pANrcAt165V96JV3aVS6oNLfn6vJZ/W5gwODtptgmVEQz7OG+muWT9IwjGbrGot9ehcbcK39NDjRL9p/aTvRhrJrEUj53T3cIQtsSD/+vwUr85k+cdnJ7lkc4YbzhwgEthYg5pm4ZZr16p7Ub1ZQOeKyPtF5M7q/66cE+iUEcACkYCX80YiBH3L47sq7/wnPSuoR2erJ3yb9XmNntOeoI93XTLKTWcP4PMIT42l+PSDR7hvf5x8yVmd7MA9165V96I1HYAYfBF4DqMi6C8CHwWeFZG/EWmXmSFrKBY7b+Vk2O/l/JFuwks7SlXaL/ujIerQudrEbrMmfJv2eRs4pyLCldtjvP/KLZwxECZfVtx/IM6nHjzCQ4fnKDarM1EjuOTatepetN4I4LeBNwBXKaV2KKWuVkptB67G6AfwfkusaBPacR1APQSqReS6g8aw3i251PXobHXV0mZ9nhXndLg7wLsv3cR7L9/E9t4gmWKFe/bN8mc/PcrPjiUpW9SPYiO45dpt1TqAdwN3KKUeX/pi9fd/X93uGtp5HcB6+L1GH9qBLj/+bJy+sI/hbj+bogG29wY5rT/MWYNdnDsS4byRzii2VU/OeODG6wnfeccpE75AU0pBrPZ5G51gtjI/fmdfmPddvpnfuHiU0Z4AyXyZb784zWceOsqzJ1JU1swxbi56HYA51suZOhdj1W8tHgD+zhIr2oRIpDNufKvh9QhnDXUxLX0MDq6u1QlPelYg/voWxq2sWtrsUhDNqJJar9a6jyfGtXLGYJgXJtLcuz/OTKbIXXsnefBQgD1n9HHWYBetjhJbrdOpWHUvWm8E4FVK1aw6VH3dgu4j7YPX68zMB6uxSmdfg9VIW4Y0dvm2YymIRrWuh0eE80e7uf3qrdx87iCxkJeJVIF/eHqCLzw+xsHZbFM+d1WapNNpWPUdXe8b6heRNwKruXGHf8OtJZlM0tfXZ7cZTccqnUPdAYoVRSpfbsripo2iChkk3GP+fRZm6tT7d9no369RrfXi9QiXbolywWg3Txyf58cH4hxL5PnSEyc4rT/MnjP62Bpr/tN5s3U6Bau+o+vdwCeBL66z3TUMDTl4qb+FWKlzZ1+IJ7/0bUdWz5Su3sbeZ1EpiHpDSVaEnBrVaha/18NV22NcsrmHR44keOhwggOzWQ48luWc4S6uP72f4e7mrahvlU67seo7uuZ4SSm1Uym1a61/lljRJszOumOCyUqdPUEfxc85M2SissmG3mdVpk69oSQrQk6Nam2UoM/D60/r48PXbeO6nb34PcKLkxk++/Axvr53ktlMc1KqW63TLqz6jtoeMBORd4jI8yJSEZHL7bZnLZSN2Q2txGqdpXGnVs9sTKdVmTr1hpKsCTnZc+12+b3ccGY/H75uG6/ZFsUj8MyJFH/+0FG+8+I0Scub0ejvqBmcEMPfC9wC/F+7DVkPHQJqjNCWYXLHJk55PbB5GJ9HKNmUVbSRcIEVmTr1hpKsCDnZHRrpCfr4+d2DXLMjxv374zxzIsXjx5I8NTbPlduiXLezly4LykvYrbNVtCQE1AqUUi8qpV622456mJg49SbWiTSqM+T3MBDxs6MvxLkjEXqr1SPPuvMDeMLLQyaecJDdf/ABdvXXNzHo91qfTqjSccuPaYZ6Q0lWhJzs1rpAX9jP284f5neu3sq5wxFKFcVPDyf41E+PcP+BOLmiuVXFK1tz5r/13SZZ7iysuhc5YQTQNnR3d9ttQkuoR2fI5yES8BIJeOkOGv/7PLVv0pvf/hYAXvr45yicmCS0ZZiz7vwAm9/+lrpjwf1hPxMpa1tySsDe/g4LI4j1snvq3W8t7Na6kuHuAO+8aITjiTz37p/l1Zks9+2P85ODc5w5GOb8kW7OGuoisFrJWmpPjuc/9SU8XTHbM8yajVX3opY4ABH5IVBr7fJHlVLfrOcYk5OT3Hbbbfh8PsrlMrfccgu333474+PjRCIRvF4vyWSSoaEhZmdnUUoxNDTExMTE4h8rlUoxMjLC1NQUIkJ/fz9TU1NEo1HK5TLpdJrR0VHGx8fx+/3EYjGmp6eJxWIUCgVmZ2fp7u5mfHycQCBAT08PMzMz9PX1kc1myeVyi+8PhUKEw2Hi8TgDAwPMz89TKBQWt4fDYQKBAIlEgsHBQRKJBMVicXF7qzRls9nF7Quapqam8Pv9a2o6c2CA+fkEhUyB0Ogox48eX1NT8fLdXPj9v1rU1Ds0xLFjx0gXSigVQaXjizcpVcgikT5UZg4QJBylODeLKvpBVVDFHNLdb6z69HiRYDcqm0CCEVS5BKX8ye1eHxLoQmWTSKgbVSpAqYB091NJzyGVMuILoHIpJBw1moqXSyff7wsiXh8qn0bCMaPYWKW8uF38IRCPkX7Y1VudhFTGz+toUpk5/K+/Av/rLlumqTI/fYom39Xn0XPDF5ZpqiQmTtFk2Bw4RZPKpfEEwi3RJIGuus/T5u5+fuN0D4dGIvx4rMjBuQIvTmZ4cTKD3wNnD3ZxXqzMGQNh/IHgMk25v/jiqZPj+QK5z34J37UX2qapnmtvtfO02rWXTnuZTyYXv09TU1Mopeq6R6x5b3bKxKaI3A98RCn1s1rbH374YbV7927Txx276x72feJz5I4vf/JshMOHD7Njx46G3ttOtFJnRSkm5gscT+ZPKUu9lNdsi/LMiXnyJeuu10piAk9sxLLjtRKz6wLaRWsyV+L5iTR7J1IcS5y8uQd9wjlDEc4f7ea0/jBej5C46iZqtrYTIfbI91podfO5cnt0WVc2M9/RJ5988ok9e/bUTLDp6BDQ2F33sPcjn6SSNS6k3LEJ9n7kkwANOYGREed/gayglTo9ImyKBhnuDnA8mWd8Ps9qxSX7u/ycSFoXBpJIey7qa2RdQLtojYZ8XL0jxtU7YsSzRcMZjKc4MV/g6RMpnj6RIhLw8pqtUS4ZHoQamVCObs1pEVZ9R22fBBaRt4nIMYwKo98VkXusOva+T3xu8ea/QCWbZ98nPtfQ8aam7E5bbA126PR6hO29IS7e1MNwt59aJWT6w35LP9MY5rcfjawLaEetfWE/1+3s5QNXbeVD12zljaf3MRTxky6Uue9AnLtf9/OUV7ZGDAaaVqnVSVj1HbV9BKCU+gbwjWYcO3e89kLl1V5fD7e0P7BTZ8Dn4fSBLjb1lDkylyOePZknHg35CHiFwhqhInO05/lsbF1Ae2pdYDAS4A2nBXj9rl4OxXM8dDjBCxddQVkpXvuDb9GTiMPwEKH3vb3jJ4DBuu+o7Q6gmayWfx7aMtzQ8fr73VFr3Ak6uwJedg9HSOZKHJ7LLb7e3+VnfN6aMJCEo5Ycp9U0si6gXbWuRETY1R9mV3+YqVSBh7b08DeXvIayAo/ApZsivCFfoifY0bc2y76jtoeAmslq+edn3fmBho6nQ0CtJxryccFoN95qiulAl3VhoHYMi0Bj6wLaVetaDHUHuPncIf79ddu5dEsPSsHPxtJ8+sGj/OjVWXIObFlpFR0TAmomCxO9VmUBRaOd8RS1Hk7W2RP04vdKzYwhj4CZRcUS6LLQstbRyLqAdtVaD9GQj5vPHeKa7TF++PIkL80W+PHBOX52LMnrdvVxxbboqmtU2hWrvqMd7QDAcAKN3vBXUi67o9+ok3WKCAM1wkBejzFpOJ02UWRMte8ToulSFG2stV6GugP86jk9HC0E+cErsxyZy3H3vhkeOZLg+jP6uGC0e1kqZTtj1Xe0o0NAVpNOp+02oSU4XWd/jTDQaE/Q9FOeKubW36lDcItWVcyxvTfE+y7fxK9fPMJQxM9crsTX907xfx85zivTmY4o6mjVd7TjRwBW0qlN4VfidJ3RFWEgrwc29RjrCERqrw2qRb0NxJ3YzMYsbmmWvqBTRDh7KMKZg108M5bi3v2zjKcK/P1T4+zqC3HDmf1saUGDmmbRqqbwmiV0clP4pThdp4gsazc50h3A7/Wwsy/MVdtjXLk9ymu2Rblsa8+aReTqaSC+sOhKjU+CUouLrqxqAt8q3NIsfaVOjwiXbOnhjmu38eYz+wn7PByM5/j8Y2P807MTzJgJGToIq76j2gGYwO+3diGSU2kHnX3VRWFeD2yKrsj0EsHrkTULiRk7rl9+uC37/9aiDq0dwSo6/V4P1+7srTaoieHzCM9PpPnMw0Zfgvm81X0JmotV31EdAjJBLBaz24SW0A46F8L9Q5HA+jf6VZDg+sWyrOz/ayf1aO0E1tMZ9nu54cwBXrPN6Evw1Ng8jx9L8vTYPNfsiHHNzl5CPuc/F1v1HXW+UgcxPT1ttwktoV10egS2rHj6X8la08Iqm1j3M1ZbXNVu9Wbq0doJ1KszFvJx83lD/M7VW9k91EWxonjg4ByffvAIjxxJ2NakqF6s+o5qB2CCdngytoJ20TncHSCwztPauSMResO1B7oSjKz7GVb1/7WberR2AmZ1DncH+LWLR7ntis1siwXJFCv828szfOahozx7IkXFoRlDVn1HdQjIBIWCtQ1JnEo76PSIsHmdp38whvznDEeYyRQ5HM8uKyetyqV1K+RY0YzFCdSjtRNoVOf23hC3XbGZl6cy/PDVWabSRe7aO8lPDwe44cx+Tu8PO6oWmFXfUe0ATJDNZu02oSW0g86eoNfUF3Kgy09vyMeJ+TyTqYLhCEr59d+INf1/badOrW3PBnSKCLuHq6mjJ+a5b3+c8fkCf/fkOKf1h3nTGf1sia3/0NEKrPqOagdgAqfnx1tFO+hs5GnM6xG2xkJsjYVIF8pMR4RUycN8oVz32oF2xW3rADaC1yNcuiXKBaPdPHo0yU8OznFgNsvnHzvO+SMRrj+j39KaVI2g1wHYgNPz463CDTojAS+k45w32s1lW3o4YzDMYMRvajVxl799vj5uXQewEfxeD9dVU0ev3WGkju6dSPOZh47y3ZemSdmYOmrVd1SPAEwQWNl8okNxm06/18NQJMBQJIBSivl8mXi2yFy2RKa4eg2d/i4/gwJH5togvOJ1yVe9CTq7/F7efNYAV26Pcd/+WZ4eS/HY0SWpozt6CbY4ddSq76hLrgpr6OnpsduEluBmnSJCNOQjGvKxow9ypQrxbJF4pkQid+oT35ZYiGJFWdqqshl0cjXQpTRTZyzk45fOG+bqHb386JVZXp7OcP+BOR4/muT1p/Vx2dbWVR216jvaPmNYBzAzM2O3CS1B6zxJyOdhU0+QnX2r143Z2RdmKOLs1dMqm7TbhJbQCp0j3QF+/ZJR3nf5JrbFgqSLFb5XTR19brw1qaNWfUe1AzBBX197NNbeKFrn+gS8Qix0cgB9+kB4WX0ipyEhl6wEbqHOHX1hbrtiM7960QiDET/xbIl/eW6Szz96nP0zmaZ+tlXfUe0ATNAO6ZFWoHWujgiM9gS4eHMP0SUOQEQ4c7CLaNCZNXdUydkhKqtotU4R4ZzhCL9z1VZ+8ZxBeoJeTswX+Nsnx/nbJ04wlmzO/JBOA7WBXM4dNdW1ztpEAh5O6w/TvUq/Wa9HOHs4wgsTKdIFhzVgcYkDsEun1yNctjXKBZu6efRIkgcPzbF/Nsv+R43U0T1n9NfsY9EoVn1H9QjABO2QH28FWuephPweLhjtXvXmv4DPI+weijiuoJheB9AaAl4Pr93Vy4ev3cY1O2J4BfZOpPnzhdTRgjWdvPQ6ABtwQ348aJ218IjUvfgs4POwe7hrzV4ErUavA2gtXQEvbzlrgDuu3cbFm7pRCh47muTTDx7h/v1x8htsWK/7AdhAKNS+HYTMoHVunIUaRI5pRu5zx9oOp+nsDft52/nDfPCqrZw12EWhrLjvQJxP//Qojx1NUG6w6qhV1652ACYIh8N2m9AStE5riAS8nD3URYPtCixFHHZjbBZO1TnSE+A3LhnlvZdvYmssSLpQ5rsvGamjextIHbXq2nXApdk+xONxu01oCVqndURDPs4b6SboW3skMNoTINDEkJHKpUztX7j7XpI330riqptI3nxr27TANKuz1ezsC/NbV2zmnReOMNDlZzZb4p+fm+SvHhvjwGz9mT1WXbs6C8gEAwMDdpvQErROa4kEvJw/2s2+qQzz+dqTgANdfnb0hRifLzCWzC82vLcKCUfr3nehD/JCK8yFPsiA46uimtFpFyLCuSMRzh7q4qmxee7fH2csmefLT5zgjIEwbzqzn009a1cdtera1SMAE8zPz9ttQkvQOq0n4PVw7kiEgTVWDC/0OLhkc8+6nc7Mogr1L0xyah/kekYlZnTajdcjXL41yh3XbWPPGX0EfcKrM1k+98hx7npuknh29Yb1Vl27egRggnZolGIFWmdz8Ihw1mAXR3w5jidWXyDk9QibogGOW7mIqFx/5Uon9kGue1RiQqdTCHg9vG5XH5dtifKTg3M8djTBs+Mpnp9IccW2KK/bdeqqX6uuXT0CMIHOj+8s7NK5vTfE6QNhWpkgZCY/3ol9kOsdldi9DmAjRAJebjx7gA9du42LNnVTUfDIESN19CtPjZMtngwf6nUANqDz4zsLO3UOdwc4ZzjSsrUCZvLjndgHud5RiVPWAWyEvrCfW84f5gNXbeHMgTD5suKrT08wlz05utH9AGxAp0d2FnbrNDKEIrw01YK4ta/+OQUn9kGWkSHU+GTN15dhQqfTGe0J8q5LN3FwNkvI72HTknkhq65d7QBM4LZGKZ2OE3SG/V4uGO2msmJBkEeE3rCPZK5Eg2uFliEmG6U4rQ9y6IO3LpsDMF48dVRiVmc7sKs/zJXbl2c3WXXt6hCQCRKJhN0mtASts7X4PEJgRe0gr8eoMnnZ1ihnDoYZiPg3tKBM5dMbtNJeAjdeT/jOO5DRYRBBRocJ33nHKU6q3XXWi1XXbue5yyYyODhotwktQet0Dj6PMBgJMBgJUFGKdKFMKl9mPl9mPl+iUOd6AQnHmmxp86lnVNIJOuvBqmtXjwBM4JQnxmajdToTjwg9QR+bokHOGurisq1Rzhrsop4adSrv7BWyVuEWnVZdu9oBmKBYXH1hRiehdbYPAxE/pw/UMSFYsaYMsePpMJ0Li9++v/k67r/8bYzddQ9g3bWrQ0Am0PnxnUWn6ByKBKhUWLOWTDvnx5uhk3SuXPyWOzbB3o98EoDRX3iDJZ+hRwAm0PnxnUUn6RzpCazZuL4T8uProZN01lr8Vsnm2feJz+l+AHYQiUTsNqElaJ3tyaZokG29tfPgxe+OHg+dpHO1xW+545OWXbvaAZjA63Vmw2+r0Trbl62xUO1CcuKSr3oH6Vyt9EZoy7Bl127n/LVaQDKZtNuElqB1tjfb+0KM9ixfKNROVTI3QifprFWSwxMOctadH7Ds2rV9ElhE/jfwVqAA7Afeq5Sas9WoVRgasq8YVivROtufXf1hKkoxmTKyRaSrd0PHK9x9r6NKQ6zGRnU6iZUlOUJbhjnrzg+w+e1vIZOxxtHZ7gCAHwB3KqVKIvK/gDuB/2SzTTWZnZ2lq6vLbjOajtbZGZzWH6aiYDpdRGWTiL8xh9dODWI2otOJLCx+u3J7FM+SBR9WXbu2h4CUUt9XSi2UuXsE2GqnPWuhTPbtbFe0zs5ARDhjIMxAlx9oXKtTG8TUprPP6QJWXbtOGAEs5X3AP9ptxGp0cshgKVpn5yAinDXURa9/E1N5SObML5RyYoOY1eikENBaWHXttsQBiMgPgVqrbj6qlPpmdZ+PAiXgK7WOMTk5yW233YbP56NcLnPLLbdw++23Mz4+TiQSwev1kkwmGRoaYnZ2FqUUQ0NDTExM0N3dDUAqlWJkZISpqSlEhP7+fqampohGo5TLZdLpNKOjo4yPj+P3+4nFYkxPTxOLxSgUCoyPj3P22WczPj5OIBCgp6eHmZkZ+vr6yGaz5HK5xfeHQiHC4TDxeJyBgQHm5+cpFAqL28PhMIFAgEQiweDgIIlEgmKxuLi9VZqy2ezi9gVNBw4cYOfOnR2lqdZ52rdvHyMjIx2labXzNDU1xdlnn83hY1NklI9M2YPKp5FwzCifUCkj3f2o1KyRSikeVCGDdPUiQ/2oyZlTv9dD/ahSAZWZAwQJR1GZOSTQBaqCKuYWj4nHiwS7UdkEEoygyiUo5U9u9/qQQJcRwgl1o0oFKBVObvcFEF8AlUsZn1PIQLm0ZHsQ8fqoJCbw9G1ZV5PKJgFl/JyOIwFjNbUqZJFIn6M0qXyadNrLfDK5eO3t27ePzZs313XtrXlvdsIwWETeA7wf2KOUqjm78fDDD6vdu3e31K6VzMzMuKJhutbZeazUmi6UOZ7MM5spst4tYOUcAAChYM1qnHajssm2aAxvlpVzAGau3SeffPKJPXv2XF5rm+0hIBG5EfiPwOtXu/lrNBpriQS8nDXYRbZYZiyZZzpdXLXvgBMbxGiswXYHAHwGCAI/EMPDPaKU+oC9JtUmlUq54olR6+w8VtMa9ns5faCLrbEKY8k8U+kC5cqp73dag5jVUIVsR44AVmLVtWu7A1BKnWG3DfUyMjJitwktQevsPNbTGvR52NUfZmssyIn5AhPzBUpWtCJrMRLps9uElmDVtWt7Gmg7MTXlvKyHZqB1dh71avV7PWzvDXHJlh629wY31IXMDozJ287Hqmu3zU6vvUg9nTc6AK2z8zCr1ecRtsRCXLSph/4u2wMFJnDHObXq2tUOwAT9/Z1Ta3wttM7Oo1GtQZ+Hs4cinD3URdDn/JurG+L/YN21qx2ACdwSMtA6O4+Nau3v8nPRph62xoL4vc51BDoEZI52GtvZTjTqjqcLrbPzsEKr1yNs6w2xJRZkJlNkYr7AfN5ZLRgl0Lm1nZZi1bWrHYAJymVnXezNQuvsPKzU6hFhKBJgKBIgXSgzPp9nJlOsmT7acpQTjGg+Vp1PHQIyQTqdttuElqB1dh7N0hoJGOsILt0SZUdfiJDf3luKKuZs/fxWYdX51CMAE3RKE/H10Do7j2Zr9XmEzdEgm6NB4tki4/MFErnSumUmrKaTmsKvhVXnU48ATNBJTcTXQuvsPFqptS/s55zhCBdv7mFzNIDP07pJ405qCr8WVp1PPQIwgd/vt9uElqB1dh52aA35POzoC7M1FmImY4wK0oUmz7t4Oq/Pcy2sOp/aAZggFovZbUJL0Do7Dzu1ej3CcHeA4e4A8/kSE/MFZjKrF5/bCBJcvwRyJ2DV+dQhIBNMT0/bbUJL0Do7D6do7Qn6OGOwi0u39LC9N2T54jKVTVh6PKdi1fnUIwATuOWJUevsPJym1e/1sCUWZHM0QDxbYiJVYC5bWv+N6yDBiAXWOR+rzqd2ACYoFAp2m9AStM7Ow6laRYT+Lj/9XX6yxTLxbIlErsR8vtTQugJVLrmiGpBV51M7ABNks1m7TWgJWmfn0Q5aw34vYb+XzdEgFaVI5csk8yUS2RKpQrm+OYNSfv19OgCrzqd2ACZwS9641tl5tJtWjwjRkI9oyMfWGJQrivm8MTqYTBVX7VWg1wGYQ08Cm8AteeNaZ+fR7lq9HqE37GdHX5hd/aFV99PrAMyhHYAJAoGA3Sa0BK2z8+gkrYORAL3hVYIXXncENaw6n9oBmKCnp8duE1qC1tl5dJrWnX0hai0wdks1UKvOp3YAJpiZmbHbhJagdXYenaY17PeyNRY85XWVTdpgTeux6nxqB2CCvj53NJzWOjuPTtS6KRokvKL6qITcsRLYqvOpHYAJ2iGVzgq0zs6jE7V6RDitP7zsNVVy5noHq7HqfGoHYIJczh21xrXOzqNTtUZDPoa7lxRGc4kDsOp8umPK3CLaLZe6UbTOzqOTte7oC+PzCBOpAkqvAzCFHgGYoN1zqetF6+w8OlmrzyPs6AtzyeYeuouJlvYfsAvdD8AGQqHVF6B0Elpn5+EGrX6vh5HebgaGepiYz3NivkCx3OKWZC3CqvOpHYAJwuHw+jt1AFpn5+EWreGwEQ7aEgsx2hNkIlVgLJnvOEdg1fnUISATxONxu01oCVpn5+EWrUt1eqt9ii/d0sPOfut7D9iJVedTjwBMMDAwYLcJLUHr7DzcorWWTo8Im3qCjHQHmE4XOZ7Ikys1UGvaQVh1PvUIwATz8/N2m9AStM7Owy1a19LpEaM15cWbuzljMHzKIrJ2wqrzqUcAJnBqUw2r0To7D7dorUeniDAUCTDY5Wc2U+J4Mke60F4jAt0QxgY6OZd6KVpn5+EWrWZ0iggDET8DET+zmSLHk3lS+XITrbMOvQ7ABjo5l3opWmfn4Ratjers7/JzwWg35wx3EQ16LbbKevQ6ABtwUyqdG3CLTnCP1o3q7A376Q37SeZKHE/mLWlU3wysOp/aAZigk5pqrIXW2Xm4RatVOhfaUc7nSxxP5Ik7zBHohjA2kEgk7DahJWidnYdbtFqtsyfoY/dwhAs3dTPQ5UccspTAKp16BGCCwcFBu01oCVpn5+EWrc3SGQl4OWuoi2yxzPFEnulMEWXj4mKrdOoRgAn0U1Rn4Rad4B6tzdYZ9ns5Y7CLizZ1M9ztr9mWshVYpVM7ABMUi0W7TWgJWmfn4RatrdIZ9ns5faCLizf3MNoTaLkjsEqndgAm0LnUnYVbdIJ7tLZaZ9DnYVd/mEu29LA5GsDbojuqXgdgAzqXurNwi05wj1a7dAa8nsWeBFtiwab3JNDrAGwgEonYbUJL0Do7D7dotVun3+the2+IzdEg4/N5xpvUk8AqndoBmMDrdf4KQSvQOjsPt2h1ik6fR9gaC7GpST0JrNJpewhIRP5IRJ4VkadF5Psistlum1YjmUzabUJL0Do7D7dodZrOlT0JAl5rQkNW6bTdAQD/Wyl1oVLqYuA7wB/abM+qDA0N2W1CS9A6Ow+3aHWqzoWeBJds6eH0gTAh38ZuvVbptN0BKKWWurII4NjebbOzs3ab0BK0zs7DLVqdrtOqngRW6XTEHICIfBz4TSABvLHWPpOTk9x22234fD7K5TK33HILt99+O+Pj40QiEbxeL8lkkqGhIWZnZ1FKMTQ0xMTEBN3d3QCkUilGRkaYmppCROjv72dqaopoNEq5XCadTjM6Osr4+Dh+v59YLMb09DSxWIxCoUA8HmdoaIjx8XECgQA9PT3MzMzQ19dHNpsll8stvj8UChEOh4nH4wwMDDA/P0+hUFjcHg6HCQQCJBIJBgcHSSQSFIvFxe2t0pTNZhe3L2iKx+NEo9GO0lTrPMXjcfx+f0dpWu08Lb12O0VTrfMUj8fp6+trG027IiFOTM2S9XWRSaehXEK6+1GpWfAFEa8PlU+TTnuZTyYXNcXjcUKhUF2a1rz3qhasZxaRHwK1Elc/qpT65pL97gRCSqn/unLHhx9+WO3evbuJVq5PLpcjFArZakMr0Do7D7dobWeda/UkuHJ7FM+SQkRmdD755JNP7Nmz5/Ja21oSAlJKvUkpdX6Nf99csetXgLe3wqZGmJiYsNuElqB1dh5u0drOOs30JLBKp+0hIBE5Uyn1SvXXm4GX7LRnLeoZUnUCWmfn4RatnaCznp4EVum03QEAnxSRs4EKcBj4gM32aDQaje0s7UnQrHXFTsgCens1HHShUuqtSqnjdtu0GqlUym4TWoLW2Xm4RWsn6uwJ+pAVjQis0mm7A2gnRkZG7DahJWidnYdbtGqd5tAOwARTU1N2m9AStM7Owy1atU5zaAdggpXDsE5F6+w83KJV6zSHdgAm6O/vt9uElqB1dh5u0ap1mkM7ABPo4WVn4Rad4B6tWqc5tAMwQTQatduElqB1dh5u0ap1mkM7ABOUy6cu0e5EtM7Owy1atU5zaAdggnQ6bbcJLUHr7DzcolXrNId2ACbQjbU7C7foBPdo1TrNoR2ACXRj7c7CLTrBPVq1TnNoB2CCf/3Xf7XbhJagdXYebtGqdZpDOwATfP3rX7fbhJagdXYebtGqdZpDOwATlEqnlmXtRLTOzsMtWrVOc7SkI5gV/OhHP5rCKBdtG7Ozs4P9/f3TdtrQCrTOzsMtWrXOmuzYs2dPzS7ybeMANBqNRmMtOgSk0Wg0LkU7AI1Go3Ep2gGYRET+t4i8JCLPisg3RKTXbpuagYi8Q0SeF5GKiFxutz1WIyI3isjLIvKqiPxnu+1pBiLyRRGZFJG9dtvSTERkm4jcJyIvVK/ZD9ttU7MQkZCIPCYiz1S1/reNHE87APP8ADhfKXUhsA+402Z7msVe4Bbgx3YbYjUi4gX+Avg54Fzg10TkXHutagpfAm6024gWUAJ+Xyl1LnAVcHuHnk+APHC9Uuoi4GLgRhG5qtGDaQdgEqXU95VSCzlYjwBb7bSnWSilXlRKvWy3HU3iNcCrSqkDSqkC8DXgZpttshyl1I+BWbvtaDZKqRNKqSerP88DLwJb7LWqOSiDhYbA/uq/hjN5tAPYGO8D/s1uIzSm2QIcXfL7MTr0huE2RGQncAnwqM2mNA0R8YrI08Ak8AOlVMNafZZZ1UGIyA+BWtWWPqqU+mZ1n49iDD2/0krbrKQenRpNuyAi3cBdwL9XSiXttqdZKKXKwMXV+cdviMj5SqmG5nm0A6iBUupNa20XkfcAvwDsUW28kGI9nR3McWDbkt+3Vl/TtCki4se4+X9FKeWKehBKqTkRuQ9jnqchB6BDQCYRkRuB/wj8olIqY7c9moZ4HDhTRHaJSAD4VeBbNtukaRAxOqT/NfCiUupP7LanmYjI0ELmoYiEgRuAlxo9nnYA5vkM0AP8QESeFpHP2W1QMxCRt4nIMeBq4Lsico/dNllFdRL/d4F7MCYM/0kp9by9VlmPiHwVeBg4W0SOichtdtvUJK4F3g1cX/1OPi0iN9ltVJPYBNwnIs9iPMj8QCn1nUYPpktBaDQajUvRIwCNRqNxKdoBaDQajUvRDkCj0WhcinYAGo1G41K0A9BoNBqXoh2ARrMCEVEickb158+JyP9rt00aTTPQDkDTtojIIREpiMjgitefqt7Ed270M5RSH1BK/dFGj7Ma1cVoFRH5y2Z9hkazGtoBaNqdg8CvLfwiIhcAXfaZY5rfBOLAO0UkaLcxGnehHYCm3fk7jJvoArcCf7t0BxEJisj/EZEjIjJRDeuEl2z/f0TkhIiMicj7Vrz3SyLyP6o/94nId0RkSkTi1Z+3Ltn3fhH5IxH5qYjMi8j3V45OVhxbqrb/F6AIvHXF9jdXm9YkROSzIvKAiPzWku3vE5EXq7bcIyI7TPzdNBrtADRtzyNAVETOqTZ6+VXg71fs80ngLIwGGmdglH7+Q1is7fQRjJoqZwJrFcjzAH8D7AC2A1mM0iBL+XXgvcAwEKgeezWuwyhE9zXgnzCcF1W7BoF/wWg4NAC8DFyzZPvNwB9gNO0ZAn4CfHWNz9JoTkE7AE0nsDAKuAGjts9iZc/qU/ZvA7+nlJqtNgz5nxiOAuBXgL9RSu1VSqWBj632IUqpGaXUXUqpTPU4Hwdev2K3v1FK7VNKZTFu6hevYfetwL8ppeLAP2B0dxqubrsJeF4p9fVq7aI/A8aXvPcDwCeqjXtKVU0X61GAxgzaAWg6gb/DePJ+DyvCPxhPx13AEyIyJyJzwN3V1wE2s7w5zOHVPkREukTk/4rIYRFJYrTL7K2OPBZYepPOAN2rHCsMvINqPwml1MPAkaqOU+yqlh0/tuQQO4BPL9E0Cwi6sY3GBNoBaNoepdRhjMngm4CVteCnMUI15ymleqv/YkqphRvzCZb3Bti+xkf9PnA2cKVSKgq8rvq6NGD224Ao8FkRGReRcYyb90IY6ARL2o1WRzJL248eBd6/RFOvUiqslHqoAVs0LkU7AE2ncBtGs+z00heVUhXgr4A/XQiviMgWEXlLdZd/At4jIueKSBfwX9f4jB4MZzInIv3r7LsetwJfBC7ACBNdjFHW+KJqJtN3gQtE5JdExAfczvLubZ8D7hSR86qaYiLyjg3Yo3Eh2gFoOgKl1H6l1M9W2fyfgFeBR6qhmx9iPMmjlPo34FPAvdV97l3jYz4FhDFGFY9ghJJMIyJbgD3Ap5RS40v+PVE95q1KqWmMENEfAzPAucDPgHzV7m8A/wv4WlXTXuDnGrFH4150PwCNpg0QEQ/GHMBvKKXus9seTWegRwAajUMRkbeISG91gdgfYMw1PGKzWZoOQjsAjca5XA3sxwg5vRX4pWp6qUZjCToEpNFoNC5FjwA0Go3GpWgHoNFoNC5FOwCNRqNxKdoBaDQajUvRDkCj0WhcinYAGo1G41L+fyWLNBBaTvDrAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "predictive_2 = Predictive(model, samples_2)\n",
+    "predictions_2 = predictive_2(age=torch.tensor(dset.AgeScaled.values, dtype=torch.float))[\"obs\"]\n",
+    "\n",
+    "mean_pred = torch.mean(predictions_2, axis=0)\n",
+    "hpdi_pred = stats.hpdi(predictions_2, 0.9)\n",
+    "\n",
+    "ax = plot_regression(dset.AgeScaled.values, mean_pred, hpdi_pred)\n",
+    "ax.set(xlabel=\"Median Age\", ylabel=\"Divorce rate\", title=\"Predictions with 90% CI\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Log posterior predictive density: -59.240997314453125\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    \"Log posterior predictive density: {}\".format(\n",
+    "        log_pred_density(samples_2, \n",
+    "                        model, \n",
+    "                        var_not_used='marriage',\n",
+    "                        age=torch.tensor(dset.AgeScaled.values, dtype=torch.float),\n",
+    "                        divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    "                    )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <a id='Model-3:-Predictor---Marriage-Rate-and-Median-Age-of-Marriage'>Model 3: Predictor - Marriage Rate and Median Age of Marriage</a>\n",
+    "\n",
+    "Finally, we will also model divorce rate as depending on both marriage rate as well as the median age of marriage. Note that the model's posterior predictive density is similar to Model 2 which likely indicates that the marginal information from marriage rate in predicting divorce rate is low when the median age of marriage is already known."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run NUTS\n",
+    "kernel = NUTS(model)\n",
+    "num_samples = 2000 if not smoke_test else 2\n",
+    "warmup_steps=200 if not smoke_test else 2\n",
+    "mcmc = MCMC(kernel, num_samples=num_samples, warmup_steps=warmup_steps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sample: 100%|██████████| 2200/2200 [00:16, 130.28it/s, step size=6.45e-01, acc. prob=0.906]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "         a     -0.00      0.10     -0.00     -0.16      0.17   2052.14      1.00\n",
+      "        bA     -0.60      0.15     -0.61     -0.84     -0.36   1583.09      1.00\n",
+      "        bM     -0.06      0.15     -0.06     -0.29      0.21   1526.50      1.00\n",
+      "     sigma      0.82      0.09      0.81      0.69      0.97   1719.60      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "mcmc.run(\n",
+    "    marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float), \n",
+    "    age=torch.tensor(dset.AgeScaled.values, dtype=torch.float), \n",
+    "    divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    ")\n",
+    "mcmc.summary()\n",
+    "samples_3 = mcmc.get_samples()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Log posterior predictive density: -59.04816436767578\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    \"Log posterior predictive density: {}\".format(\n",
+    "        log_pred_density(samples_3, \n",
+    "                        model, \n",
+    "                        # var_not_used='marriage',\n",
+    "                        marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float),\n",
+    "                        age=torch.tensor(dset.AgeScaled.values, dtype=torch.float),\n",
+    "                        divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    "                    )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <a id='Divorce-Rate-Residuals-by-State'>Divorce Rate Residuals by State</a>\n",
+    "\n",
+    "The regression plots above shows that the observed divorce rates for many states differs considerably from the mean regression line. To dig deeper into how the last model (Model 3) under-predicts or over-predicts for each of the states, we will plot the posterior predictive and residuals (`Observed divorce rate - Predicted divorce rate`) for each of the states."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predictions for Model 3\n",
+    "predictive_3 = Predictive(model, samples_3)\n",
+    "predictions_3 = predictive_3(marriage=dset.MarriageScaled.values, age=dset.AgeScaled.values)[\"obs\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x1152 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y = torch.arange(50)\n",
+    "\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 16))\n",
+    "pred_mean = torch.mean(predictions_3, axis=0)\n",
+    "pred_hpdi = stats.hpdi(predictions_3, 0.9)\n",
+    "residuals_3 = torch.tensor(dset.DivorceScaled.values, dtype=torch.float) - predictions_3\n",
+    "residuals_mean = torch.mean(residuals_3, axis=0)\n",
+    "residuals_hpdi = stats.hpdi(residuals_3, 0.9)\n",
+    "idx = torch.argsort(residuals_mean)\n",
+    "\n",
+    "# Plot posterior predictive\n",
+    "ax[0].plot(torch.zeros(50), y, \"--\")\n",
+    "ax[0].errorbar(\n",
+    "    pred_mean[idx],\n",
+    "    y,\n",
+    "    xerr=pred_hpdi[1, idx] - pred_mean[idx],\n",
+    "    marker=\"o\",\n",
+    "    ms=5,\n",
+    "    mew=4,\n",
+    "    ls=\"none\",\n",
+    "    alpha=0.8,\n",
+    ")\n",
+    "ax[0].plot(dset.DivorceScaled.values[idx], y, marker=\"o\", ls=\"none\", color=\"gray\")\n",
+    "ax[0].set(\n",
+    "    xlabel=\"Posterior Predictive (red) vs. Actuals (gray)\",\n",
+    "    ylabel=\"State\",\n",
+    "    title=\"Posterior Predictive with 90% CI\",\n",
+    ")\n",
+    "ax[0].set_yticks(y)\n",
+    "ax[0].set_yticklabels(dset.Loc.values[idx], fontsize=10)\n",
+    "\n",
+    "# Plot residuals\n",
+    "residuals_3 = torch.tensor(dset.DivorceScaled.values, dtype=torch.float) - predictions_3\n",
+    "residuals_mean = torch.mean(residuals_3, axis=0)\n",
+    "residuals_hpdi = stats.hpdi(residuals_3, 0.9)\n",
+    "err = residuals_hpdi[1] - residuals_mean\n",
+    "\n",
+    "ax[1].plot(torch.zeros(50), y, \"--\")\n",
+    "ax[1].errorbar(\n",
+    "    residuals_mean[idx], y, xerr=err[idx], marker=\"o\", ms=5, mew=4, ls=\"none\", alpha=0.8\n",
+    ")\n",
+    "ax[1].set(xlabel=\"Residuals\", ylabel=\"State\", title=\"Residuals with 90% CI\")\n",
+    "ax[1].set_yticks(y)\n",
+    "ax[1].set_yticklabels(dset.Loc.values[idx], fontsize=10);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot on the left shows the mean predictions with 90% CI for each of the states using Model 3. The gray markers indicate the actual observed divorce rates. The right plot shows the residuals for each of the states, and both these plots are sorted by the residuals, i.e. at the bottom, we are looking at states where the model predictions are higher than the observed rates, whereas at the top, the reverse is true.\n",
+    "\n",
+    "Overall, the model fit seems good because most observed data points like within a 90% CI around the mean predictions. However, notice how the model over-predicts by a large margin for states like Idaho (bottom left), and on the other end under-predicts for states like Maine (top right). This is likely indicative of other factors that we are missing out in our model that affect divorce rate across different states. Even ignoring other socio-political variables, one such factor that we have not yet modeled is the measurement noise given by `Divorce SE` in the dataset. We will explore this in the next section.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <a id='Regression-Model-with-Measurement-Error'>Regression Model with Measurement Error</a>\n",
+    "\n",
+    "Note that in our previous models, each data point influences the regression line equally. Is this well justified? We will build on the previous model to incorporate measurement error given by `Divorce SE` variable in the dataset. Incorporating measurement noise will be useful in ensuring that observations that have higher confidence (i.e. lower measurement noise) have a greater impact on the regression line. On the other hand, this will also help us better model outliers with high measurement errors. For more details on modeling errors due to measurement noise, refer to Chapter 14 of [[1](#References)].\n",
+    "\n",
+    "To do this, we will reuse Model 3, with the only change that the final observed value has a measurement error given by `divorce_sd` (notice that this has to be standardized since the `divorce` variable itself has been standardized to mean 0 and std 1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def model_se(marriage, age, divorce_sd, divorce=None):\n",
+    "    a = pyro.sample(\"a\", dist.Normal(0.0, 0.2))\n",
+    "    bM = pyro.sample(\"bM\", dist.Normal(0.0, 0.5))\n",
+    "    M = bM * marriage\n",
+    "    bA = pyro.sample(\"bA\", dist.Normal(0.0, 0.5))\n",
+    "    A = bA * age\n",
+    "    sigma = pyro.sample(\"sigma\", dist.Exponential(1.0))\n",
+    "    mu = a + M + A\n",
+    "    divorce_rate = pyro.sample(\"divorce_rate\", dist.Normal(mu, sigma))\n",
+    "    pyro.sample(\"obs\", dist.Normal(divorce_rate, divorce_sd), obs=divorce)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standardize\n",
+    "dset[\"DivorceScaledSD\"] = dset[\"Divorce SE\"] / np.std(dset.Divorce.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sample: 100%|██████████| 4000/4000 [01:23, 47.73it/s, step size=2.66e-01, acc. prob=0.934]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                      mean       std    median      5.0%     95.0%     n_eff     r_hat\n",
+      "               a     -0.05      0.10     -0.05     -0.22      0.10   2892.05      1.00\n",
+      "              bA     -0.61      0.16     -0.61     -0.87     -0.35   1764.01      1.00\n",
+      "              bM      0.06      0.17      0.06     -0.22      0.32   1698.03      1.00\n",
+      " divorce_rate[0]      1.15      0.37      1.14      0.52      1.73   4101.30      1.00\n",
+      " divorce_rate[1]      0.68      0.54      0.66     -0.20      1.52   4700.64      1.00\n",
+      " divorce_rate[2]      0.43      0.33      0.43     -0.09      1.00   5579.89      1.00\n",
+      " divorce_rate[3]      1.41      0.45      1.40      0.64      2.15   4928.70      1.00\n",
+      " divorce_rate[4]     -0.90      0.13     -0.90     -1.10     -0.68   5104.04      1.00\n",
+      " divorce_rate[5]      0.65      0.40      0.64      0.01      1.33   4066.76      1.00\n",
+      " divorce_rate[6]     -1.36      0.35     -1.35     -2.00     -0.83   5605.81      1.00\n",
+      " divorce_rate[7]     -0.32      0.49     -0.31     -1.12      0.47   4610.33      1.00\n",
+      " divorce_rate[8]     -1.88      0.60     -1.88     -2.85     -0.92   2866.64      1.00\n",
+      " divorce_rate[9]     -0.62      0.17     -0.62     -0.89     -0.34   5923.41      1.00\n",
+      "divorce_rate[10]      0.75      0.29      0.75      0.28      1.23   4697.33      1.00\n",
+      "divorce_rate[11]     -0.53      0.48     -0.53     -1.39      0.22   3437.37      1.00\n",
+      "divorce_rate[12]      0.21      0.49      0.22     -0.57      1.03   2057.42      1.00\n",
+      "divorce_rate[13]     -0.86      0.23     -0.86     -1.27     -0.53   7300.48      1.00\n",
+      "divorce_rate[14]      0.55      0.31      0.55      0.01      1.04   4580.96      1.00\n",
+      "divorce_rate[15]      0.28      0.38      0.29     -0.41      0.86   6245.29      1.00\n",
+      "divorce_rate[16]      0.50      0.43      0.50     -0.20      1.20   5173.23      1.00\n",
+      "divorce_rate[17]      1.24      0.35      1.23      0.72      1.85   3672.41      1.00\n",
+      "divorce_rate[18]      0.42      0.38      0.41     -0.18      1.06   5871.63      1.00\n",
+      "divorce_rate[19]      0.38      0.53      0.37     -0.48      1.24   2655.37      1.00\n",
+      "divorce_rate[20]     -0.56      0.31     -0.56     -1.05     -0.03   4313.36      1.00\n",
+      "divorce_rate[21]     -1.10      0.27     -1.10     -1.51     -0.64   4575.67      1.00\n",
+      "divorce_rate[22]     -0.27      0.26     -0.27     -0.70      0.15   5345.20      1.00\n",
+      "divorce_rate[23]     -1.00      0.30     -1.00     -1.47     -0.50   4594.95      1.00\n",
+      "divorce_rate[24]      0.42      0.41      0.40     -0.24      1.08   4888.42      1.00\n",
+      "divorce_rate[25]     -0.03      0.30     -0.02     -0.52      0.47   5341.36      1.00\n",
+      "divorce_rate[26]     -0.03      0.50     -0.03     -0.78      0.84   4283.69      1.00\n",
+      "divorce_rate[27]     -0.15      0.39     -0.14     -0.78      0.49   4833.08      1.00\n",
+      "divorce_rate[28]     -0.27      0.49     -0.27     -1.08      0.54   3475.12      1.00\n",
+      "divorce_rate[29]     -1.79      0.23     -1.79     -2.17     -1.41   5616.94      1.00\n",
+      "divorce_rate[30]      0.18      0.42      0.17     -0.53      0.85   5533.95      1.00\n",
+      "divorce_rate[31]     -1.66      0.17     -1.66     -1.94     -1.39   5818.72      1.00\n",
+      "divorce_rate[32]      0.12      0.24      0.12     -0.28      0.51   5921.18      1.00\n",
+      "divorce_rate[33]     -0.03      0.50     -0.00     -0.82      0.84   3123.85      1.00\n",
+      "divorce_rate[34]     -0.12      0.22     -0.12     -0.49      0.22   4427.76      1.00\n",
+      "divorce_rate[35]      1.26      0.40      1.25      0.61      1.90   4695.57      1.00\n",
+      "divorce_rate[36]      0.22      0.35      0.22     -0.34      0.82   5018.34      1.00\n",
+      "divorce_rate[37]     -1.03      0.22     -1.03     -1.36     -0.66   4955.84      1.00\n",
+      "divorce_rate[38]     -0.93      0.54     -0.95     -1.91     -0.12   3953.88      1.00\n",
+      "divorce_rate[39]     -0.68      0.32     -0.67     -1.23     -0.18   6472.10      1.00\n",
+      "divorce_rate[40]      0.24      0.54      0.24     -0.59      1.20   4899.75      1.00\n",
+      "divorce_rate[41]      0.74      0.34      0.73      0.17      1.30   3620.41      1.00\n",
+      "divorce_rate[42]      0.20      0.18      0.19     -0.10      0.51   6072.49      1.00\n",
+      "divorce_rate[43]      0.82      0.43      0.82      0.13      1.54   3248.31      1.00\n",
+      "divorce_rate[44]     -0.42      0.53     -0.41     -1.27      0.44   4783.05      1.00\n",
+      "divorce_rate[45]     -0.39      0.26     -0.39     -0.87     -0.01   6733.07      1.00\n",
+      "divorce_rate[46]      0.13      0.29      0.13     -0.37      0.58   6125.59      1.00\n",
+      "divorce_rate[47]      0.56      0.46      0.55     -0.15      1.32   5691.95      1.00\n",
+      "divorce_rate[48]     -0.63      0.28     -0.63     -1.11     -0.21   4968.47      1.00\n",
+      "divorce_rate[49]      0.87      0.57      0.88     -0.09      1.78   2810.28      1.00\n",
+      "           sigma      0.58      0.11      0.57      0.38      0.73   1217.76      1.00\n",
+      "\n",
+      "Number of divergences: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_samples=3000 if not smoke_test else 2\n",
+    "warmup_steps=1000 if not smoke_test else 2\n",
+    "kernel = NUTS(model_se, target_accept_prob=0.9)\n",
+    "mcmc = MCMC(kernel, warmup_steps=warmup_steps, num_samples=num_samples)\n",
+    "mcmc.run(\n",
+    "    marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float), \n",
+    "    age=torch.tensor(dset.AgeScaled.values, dtype=torch.float), \n",
+    "    divorce_sd=torch.tensor(dset.DivorceScaledSD, dtype=torch.float),\n",
+    "    divorce=torch.tensor(dset.DivorceScaled.values, dtype=torch.float)\n",
+    ")\n",
+    "mcmc.summary()\n",
+    "samples_4 = mcmc.get_samples()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <a id='Effect-of-Incorporating-Measurement-Noise-on-Residuals'>Effect of Incorporating Measurement Noise on Residuals</a>\n",
+    "\n",
+    "Notice that our values for the regression coefficients is very similar to Model 3. However, introducing measurement noise allows us to more closely match our predictive distribution to the observed values. We can see this if we plot the residuals as earlier. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predictions for Model SE\n",
+    "predictive_4 = Predictive(model_se, samples_4)\n",
+    "predictions_4 = predictive_4(\n",
+    "    marriage=torch.tensor(dset.MarriageScaled.values, dtype=torch.float), \n",
+    "    age=torch.tensor(dset.AgeScaled.values, dtype=torch.float),\n",
+    "    divorce_sd=torch.tensor(dset.DivorceScaledSD.values, dtype=torch.float),\n",
+    "    )[\"obs\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x1152 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sd = torch.tensor(dset.DivorceScaledSD.values, dtype=torch.float)\n",
+    "residuals_4 = torch.tensor(dset.DivorceScaled.values, dtype=torch.float) - predictions_4\n",
+    "residuals_mean = torch.mean(residuals_4, axis=0)\n",
+    "residuals_hpdi = stats.hpdi(residuals_4, 0.9)\n",
+    "err = residuals_hpdi[1] - residuals_mean\n",
+    "idx = torch.argsort(residuals_mean)\n",
+    "y = torch.arange(50)\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 16))\n",
+    "\n",
+    "\n",
+    "# Plot Residuals\n",
+    "ax.plot(torch.zeros(50), y, \"--\")\n",
+    "ax.errorbar(\n",
+    "    residuals_mean[idx], y, xerr=err[idx], marker=\"o\", ms=5, mew=4, ls=\"none\", alpha=0.8\n",
+    ")\n",
+    "\n",
+    "# Plot SD\n",
+    "ax.errorbar(residuals_mean[idx], y, xerr=sd[idx], ls=\"none\", color=\"orange\", alpha=0.9)\n",
+    "\n",
+    "# Plot earlier mean residual\n",
+    "ax.plot(\n",
+    "    torch.mean(torch.tensor(dset.DivorceScaled.values, dtype=torch.float) - predictions_3, 0)[idx],\n",
+    "    y,\n",
+    "    ls=\"none\",\n",
+    "    marker=\"o\",\n",
+    "    ms=6,\n",
+    "    color=\"black\",\n",
+    "    alpha=0.6,\n",
+    ")\n",
+    "\n",
+    "ax.set(xlabel=\"Residuals\", ylabel=\"State\", title=\"Residuals with 90% CI\")\n",
+    "ax.set_yticks(y)\n",
+    "ax.set_yticklabels(dset.Loc.values[idx], fontsize=10)\n",
+    "ax.text(\n",
+    "    -2.8,\n",
+    "    -7,\n",
+    "    \"Residuals (with error-bars) from current model (in red). \"\n",
+    "    \"Black marker \\nshows residuals from the previous model (Model 3). \"\n",
+    "    \"Measurement \\nerror is indicated by orange bar.\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot above shows the residuals for each of the states, along with the measurement noise given by inner error bar. The gray dots are the mean residuals from our earlier Model 3. Notice how having an additional degree of freedom to model the measurement noise has shrunk the residuals. In particular, for Idaho and Maine, our predictions are now much closer to the observed values after incorporating measurement noise in the model.\n",
+    "\n",
+    "To better see how measurement noise affects the movement of the regression line, let us plot the residuals with respect to the measurement noise.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 6))\n",
+    "x = torch.tensor(dset.DivorceScaledSD.values, dtype=torch.float)\n",
+    "y1 = torch.mean(residuals_3, 0)\n",
+    "y2 = torch.mean(residuals_4, 0)\n",
+    "ax.plot(x, y1, ls=\"none\", marker=\"o\")\n",
+    "ax.plot(x, y2, ls=\"none\", marker=\"o\")\n",
+    "for i, (j, k) in enumerate(zip(y1, y2)):\n",
+    "    ax.plot([x[i], x[i]], [j, k], \"--\", color=\"gray\")\n",
+    "\n",
+    "ax.set(\n",
+    "    xlabel=\"Measurement Noise\",\n",
+    "    ylabel=\"Residual\",\n",
+    "    title=\"Mean residuals (Model 4: red, Model 3: blue)\",\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot above shows what has happened in more detail - the regression line itself has moved to ensure a better fit for observations with low measurement noise (left of the plot) where the residuals have shrunk very close to 0. That is to say that data points with low measurement error have a concomitantly higher contribution in determining the regression line. On the other hand, for states with high measurement error (right of the plot), incorporating measurement noise allows us to move our posterior distribution mass closer to the observations resulting in a shrinkage of residuals as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## <a id='References'>References<a/>\n",
+    "\n",
+    "1. McElreath, R. (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan CRC Press.\n",
+    "2. Stan Development Team. [Stan User's Guide](https://mc-stan.org/docs/2_19/stan-users-guide/index.html)\n",
+    "3. Goodman, N.D., and StuhlMueller, A. (2014). [The Design and Implementation of Probabilistic Programming Languages](http://dippl.org/)\n",
+    "4. Pyro Development Team. [Poutine: A Guide to Programming with Effect Handlers in Pyro](http://pyro.ai/examples/effect_handlers.html)\n",
+    "5. Hoffman, M.D., Gelman, A. (2011). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.\n",
+    "6. Betancourt, M. (2017). A Conceptual Introduction to Hamiltonian Monte Carlo.\n",
+    "7. JAX Development Team (2018). [Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more](https://github.com/google/jax)\n",
+    "8. Gelman, A., Hwang, J., and Vehtari A. [Understanding predictive information criteria for Bayesian models](https://arxiv.org/pdf/1307.5928.pdf)"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "d532b24ec25e32c28b9b5574ecac14ce96304d76bd8f63355b24332e4c441843"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.9 64-bit ('probabilistic_ml': conda)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}